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GEM-SciDuet-train-131#paper-1354#slide-17 | 1354 | Neural Argument Generation Augmented with Externally Retrieved Evidence | High quality arguments are essential elements for human reasoning and decision-making processes. However, effective argument construction is a challenging task for both human and machines. In this work, we study a novel task on automatically generating arguments of a different stance for a given statement. We propose a... | {
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"Model... | GEM-SciDuet-train-131#paper-1354#slide-17 | Sample Argument | Original Post Generated Counterargument
Putin is trying to re-form a Soviet
Union with his past actions in Georgia and current actions in Ukraine.
I firmly believe that Putin and the
Russian Federation (RF) are trying to re-form a Soviet Union type regime
The Russian Army invaded certain regions of Georgia
There are tw... | Original Post Generated Counterargument
Putin is trying to re-form a Soviet
Union with his past actions in Georgia and current actions in Ukraine.
I firmly believe that Putin and the
Russian Federation (RF) are trying to re-form a Soviet Union type regime
The Russian Army invaded certain regions of Georgia
There are tw... | [] |
GEM-SciDuet-train-131#paper-1354#slide-18 | 1354 | Neural Argument Generation Augmented with Externally Retrieved Evidence | High quality arguments are essential elements for human reasoning and decision-making processes. However, effective argument construction is a challenging task for both human and machines. In this work, we study a novel task on automatically generating arguments of a different stance for a given statement. We propose a... | {
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"Model... | GEM-SciDuet-train-131#paper-1354#slide-18 | Future Directions | Better evidence retrieval system
Prone to incoherence, inaccurate information, generic generation etc | Better evidence retrieval system
Prone to incoherence, inaccurate information, generic generation etc | [] |
GEM-SciDuet-train-131#paper-1354#slide-19 | 1354 | Neural Argument Generation Augmented with Externally Retrieved Evidence | High quality arguments are essential elements for human reasoning and decision-making processes. However, effective argument construction is a challenging task for both human and machines. In this work, we study a novel task on automatically generating arguments of a different stance for a given statement. We propose a... | {
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"Model... | GEM-SciDuet-train-131#paper-1354#slide-19 | Conclusion | We study a novel neural argument generation task.
We collect and release a new dataset from r/ChangeMyView and accompanying Wikipedia evidence for argument generation research.
We propose an end-to-end argument generation system, enhanced with Wikipedia retrieved evidence sentences. | We study a novel neural argument generation task.
We collect and release a new dataset from r/ChangeMyView and accompanying Wikipedia evidence for argument generation research.
We propose an end-to-end argument generation system, enhanced with Wikipedia retrieved evidence sentences. | [] |
GEM-SciDuet-train-132#paper-1355#slide-0 | 1355 | Towards Understanding the Geometry of Knowledge Graph Embeddings | Knowledge Graph (KG) embedding has emerged as a very active area of research over the last few years, resulting in the development of several embedding methods. These KG embedding methods represent KG entities and relations as vectors in a high-dimensional space. Despite this popularity and effectiveness of KG embeddin... | {
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"Multiplicative KG Embedding ... | GEM-SciDuet-train-132#paper-1355#slide-0 | Knowledge Graphs KG | Football Team Lionel Messi | Football Team Lionel Messi | [] |
GEM-SciDuet-train-132#paper-1355#slide-1 | 1355 | Towards Understanding the Geometry of Knowledge Graph Embeddings | Knowledge Graph (KG) embedding has emerged as a very active area of research over the last few years, resulting in the development of several embedding methods. These KG embedding methods represent KG entities and relations as vectors in a high-dimensional space. Despite this popularity and effectiveness of KG embeddin... | {
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"Multiplicative KG Embedding ... | GEM-SciDuet-train-132#paper-1355#slide-1 | KG Embeddings | Represents entities and relations as vectors in a vector space
1. Translating Embeddings for Modeling Multi-relational Data, Bordes et al. NIPS 2013. | Represents entities and relations as vectors in a vector space
1. Translating Embeddings for Modeling Multi-relational Data, Bordes et al. NIPS 2013. | [] |
GEM-SciDuet-train-132#paper-1355#slide-2 | 1355 | Towards Understanding the Geometry of Knowledge Graph Embeddings | Knowledge Graph (KG) embedding has emerged as a very active area of research over the last few years, resulting in the development of several embedding methods. These KG embedding methods represent KG entities and relations as vectors in a high-dimensional space. Despite this popularity and effectiveness of KG embeddin... | {
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"Multiplicative KG Embedding ... | GEM-SciDuet-train-132#paper-1355#slide-2 | Geometry of Embeddings | Arrangement of vectors in the vector space.
A recent work by (Mimno and Thompson, 2017)1 presented an analysis of the geometry of word embeddings and revealed interesting results.
However, geometrical understanding of KG embeddings is very limited, despite their popularity.
1. The strange geometry of skip-gram with neg... | Arrangement of vectors in the vector space.
A recent work by (Mimno and Thompson, 2017)1 presented an analysis of the geometry of word embeddings and revealed interesting results.
However, geometrical understanding of KG embeddings is very limited, despite their popularity.
1. The strange geometry of skip-gram with neg... | [] |
GEM-SciDuet-train-132#paper-1355#slide-3 | 1355 | Towards Understanding the Geometry of Knowledge Graph Embeddings | Knowledge Graph (KG) embedding has emerged as a very active area of research over the last few years, resulting in the development of several embedding methods. These KG embedding methods represent KG entities and relations as vectors in a high-dimensional space. Despite this popularity and effectiveness of KG embeddin... | {
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"Multiplicative KG Embedding ... | GEM-SciDuet-train-132#paper-1355#slide-3 | Problem | Study the geometrical behavior of KG embeddings learnt by different methods.
Study the effect of various hyper-parameters used during training on the geometry of KG embeddings.
Study the correlation between the geometry and performance of KG embeddings. | Study the geometrical behavior of KG embeddings learnt by different methods.
Study the effect of various hyper-parameters used during training on the geometry of KG embeddings.
Study the correlation between the geometry and performance of KG embeddings. | [] |
GEM-SciDuet-train-132#paper-1355#slide-4 | 1355 | Towards Understanding the Geometry of Knowledge Graph Embeddings | Knowledge Graph (KG) embedding has emerged as a very active area of research over the last few years, resulting in the development of several embedding methods. These KG embedding methods represent KG entities and relations as vectors in a high-dimensional space. Despite this popularity and effectiveness of KG embeddin... | {
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"Multiplicative KG Embedding ... | GEM-SciDuet-train-132#paper-1355#slide-4 | KG Embedding Methods | Learns d-dimensional vectors for entities and relations in a KG.
A score function distinguishes correct triples from incorrect triples
(Messi, plays-for-team, Barcelona) > (Messi, plays-for-team, Liverpool)
A loss function is used for training the embeddings (usually logistic loss or margin-based ranking loss).
Entry-w... | Learns d-dimensional vectors for entities and relations in a KG.
A score function distinguishes correct triples from incorrect triples
(Messi, plays-for-team, Barcelona) > (Messi, plays-for-team, Liverpool)
A loss function is used for training the embeddings (usually logistic loss or margin-based ranking loss).
Entry-w... | [] |
GEM-SciDuet-train-132#paper-1355#slide-6 | 1355 | Towards Understanding the Geometry of Knowledge Graph Embeddings | Knowledge Graph (KG) embedding has emerged as a very active area of research over the last few years, resulting in the development of several embedding methods. These KG embedding methods represent KG entities and relations as vectors in a high-dimensional space. Despite this popularity and effectiveness of KG embeddin... | {
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"Multiplicative KG Embedding ... | GEM-SciDuet-train-132#paper-1355#slide-6 | Experiments | We study the effect of following factors on the geometry of KG
Type of method (Additive or Multiplicative)
Number of Negative Samples
Dimension of Vector Space
We also study the correlation of performance and geometry. | We study the effect of following factors on the geometry of KG
Type of method (Additive or Multiplicative)
Number of Negative Samples
Dimension of Vector Space
We also study the correlation of performance and geometry. | [] |
GEM-SciDuet-train-132#paper-1355#slide-9 | 1355 | Towards Understanding the Geometry of Knowledge Graph Embeddings | Knowledge Graph (KG) embedding has emerged as a very active area of research over the last few years, resulting in the development of several embedding methods. These KG embedding methods represent KG entities and relations as vectors in a high-dimensional space. Despite this popularity and effectiveness of KG embeddin... | {
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"Multiplicative KG Embedding ... | GEM-SciDuet-train-132#paper-1355#slide-9 | Additive vs Multiplicative | Model Type Conicity Vector Spread | Model Type Conicity Vector Spread | [] |
GEM-SciDuet-train-132#paper-1355#slide-10 | 1355 | Towards Understanding the Geometry of Knowledge Graph Embeddings | Knowledge Graph (KG) embedding has emerged as a very active area of research over the last few years, resulting in the development of several embedding methods. These KG embedding methods represent KG entities and relations as vectors in a high-dimensional space. Despite this popularity and effectiveness of KG embeddin... | {
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"Multiplicative KG Embedding ... | GEM-SciDuet-train-132#paper-1355#slide-10 | Effect of Negative Samples Entity Vectors | Model Type Vector Type Conicity AVL
Entity No Change No Change
Relation No Change No Change
Multiplicative Relation Decreases No Change except HolE | Model Type Vector Type Conicity AVL
Entity No Change No Change
Relation No Change No Change
Multiplicative Relation Decreases No Change except HolE | [] |
GEM-SciDuet-train-132#paper-1355#slide-11 | 1355 | Towards Understanding the Geometry of Knowledge Graph Embeddings | Knowledge Graph (KG) embedding has emerged as a very active area of research over the last few years, resulting in the development of several embedding methods. These KG embedding methods represent KG entities and relations as vectors in a high-dimensional space. Despite this popularity and effectiveness of KG embeddin... | {
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"Multiplicative KG Embedding ... | GEM-SciDuet-train-132#paper-1355#slide-11 | SGNS Word2Vec1 as Multiplicative Model | Similar observation was made by (Mimno and Thompson, 2017)2 for
SGNS based word embeddings where higher #negatives resulted in higher conicity.
Word2Vec1 maximizes word and context vector dot product for positive word-context pairs.
This behavior is consistent with that of multiplicative models.
1. Distributed represen... | Similar observation was made by (Mimno and Thompson, 2017)2 for
SGNS based word embeddings where higher #negatives resulted in higher conicity.
Word2Vec1 maximizes word and context vector dot product for positive word-context pairs.
This behavior is consistent with that of multiplicative models.
1. Distributed represen... | [] |
GEM-SciDuet-train-132#paper-1355#slide-13 | 1355 | Towards Understanding the Geometry of Knowledge Graph Embeddings | Knowledge Graph (KG) embedding has emerged as a very active area of research over the last few years, resulting in the development of several embedding methods. These KG embedding methods represent KG entities and relations as vectors in a high-dimensional space. Despite this popularity and effectiveness of KG embeddin... | {
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"Multiplicative KG Embedding ... | GEM-SciDuet-train-132#paper-1355#slide-13 | Effect of Dimensions | Model Type Vector Type Conicity AVL
Entity No Change No Change
Relation No Change No Change | Model Type Vector Type Conicity AVL
Entity No Change No Change
Relation No Change No Change | [] |
GEM-SciDuet-train-132#paper-1355#slide-14 | 1355 | Towards Understanding the Geometry of Knowledge Graph Embeddings | Knowledge Graph (KG) embedding has emerged as a very active area of research over the last few years, resulting in the development of several embedding methods. These KG embedding methods represent KG entities and relations as vectors in a high-dimensional space. Despite this popularity and effectiveness of KG embeddin... | {
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"Multiplicative KG Embedding ... | GEM-SciDuet-train-132#paper-1355#slide-14 | Correlation b w Geometry and Performance | No correlation between geometry and performance.
For fixed number of negative samples,
Conicity has negative correlation with performance
AVL has positive correlation with performance | No correlation between geometry and performance.
For fixed number of negative samples,
Conicity has negative correlation with performance
AVL has positive correlation with performance | [] |
GEM-SciDuet-train-132#paper-1355#slide-15 | 1355 | Towards Understanding the Geometry of Knowledge Graph Embeddings | Knowledge Graph (KG) embedding has emerged as a very active area of research over the last few years, resulting in the development of several embedding methods. These KG embedding methods represent KG entities and relations as vectors in a high-dimensional space. Despite this popularity and effectiveness of KG embeddin... | {
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"Multiplicative KG Embedding ... | GEM-SciDuet-train-132#paper-1355#slide-15 | Conclusion and Future Works | We initiated the study of geometrical behavior of KG embeddings and presented various insights.
Explore whether other entity/relation features (eg entity category) have any correlation with geometry.
Explore other geometrical metrics which have better correlation with performance and use it for learning better KG embed... | We initiated the study of geometrical behavior of KG embeddings and presented various insights.
Explore whether other entity/relation features (eg entity category) have any correlation with geometry.
Explore other geometrical metrics which have better correlation with performance and use it for learning better KG embed... | [] |
GEM-SciDuet-train-133#paper-1358#slide-0 | 1358 | What makes a good conversation? How controllable attributes affect human judgments | A good conversation requires balance -between simplicity and detail; staying on topic and changing it; asking questions and answering them. Although dialogue agents are commonly evaluated via human judgments of overall quality, the relationship between quality and these individual factors is less well-studied. In this ... | {
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GEM-SciDuet-train-133#paper-1358#slide-1 | 1358 | What makes a good conversation? How controllable attributes affect human judgments | A good conversation requires balance -between simplicity and detail; staying on topic and changing it; asking questions and answering them. Although dialogue agents are commonly evaluated via human judgments of overall quality, the relationship between quality and these individual factors is less well-studied. In this ... | {
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1. How effectively can we control the different attributes?
Pretty well! But some control methods only work for some attributes.
2. How do the controllable attributes aff... | By controlling multiple attributes of generated text and human-evaluating multiple aspects of conversational quality, we aim to answer the following:
1. How effectively can we control the different attributes?
Pretty well! But some control methods only work for some attributes.
2. How do the controllable attributes aff... | [] |
GEM-SciDuet-train-133#paper-1358#slide-2 | 1358 | What makes a good conversation? How controllable attributes affect human judgments | A good conversation requires balance -between simplicity and detail; staying on topic and changing it; asking questions and answering them. Although dialogue agents are commonly evaluated via human judgments of overall quality, the relationship between quality and these individual factors is less well-studied. In this ... | {
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I have a big library at home.
I'm a museum tour guide.
I have two dogs.
I like to work on vintage cars.
My favorite music is country.
I own two vintage Mustangs.
Hello, how are you doing?
Great thanks, just listening to my favorite Johnny Cash album!
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I have a big library at home.
I'm a museum tour guide.
I have two dogs.
I like to work on vintage cars.
My favorite music is country.
I own two vintage Mustangs.
Hello, how are you doing?
Great thanks, just listening to my favorite Johnny Cash album!
Nice! I'm not much of a music fan myself, ... | [] |
GEM-SciDuet-train-133#paper-1358#slide-3 | 1358 | What makes a good conversation? How controllable attributes affect human judgments | A good conversation requires balance -between simplicity and detail; staying on topic and changing it; asking questions and answering them. Although dialogue agents are commonly evaluated via human judgments of overall quality, the relationship between quality and these individual factors is less well-studied. In this ... | {
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Goal: Respond more on-topic; don't ignore user
Goal: Find the optimal rate of question-asking | Goal: Reduce repetition (within and across utterances)
Goal: Reduce genericness of responses (e.g. oh that's cool)
Goal: Respond more on-topic; don't ignore user
Goal: Find the optimal rate of question-asking | [] |
GEM-SciDuet-train-133#paper-1358#slide-4 | 1358 | What makes a good conversation? How controllable attributes affect human judgments | A good conversation requires balance -between simplicity and detail; staying on topic and changing it; asking questions and answering them. Although dialogue agents are commonly evaluated via human judgments of overall quality, the relationship between quality and these individual factors is less well-studied. In this ... | {
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Did you find the bot interesting to talk to?
Does the bot say things that don't make sense?
Does the bot use English naturally?
Does the bot pay attention to what you say?
Does the bot ask a good amount of questions?
Is it a person or a bot?
Is it enjoyable to talk to? | Does the bot repeat itself?
Did you find the bot interesting to talk to?
Does the bot say things that don't make sense?
Does the bot use English naturally?
Does the bot pay attention to what you say?
Does the bot ask a good amount of questions?
Is it a person or a bot?
Is it enjoyable to talk to? | [] |
GEM-SciDuet-train-133#paper-1358#slide-5 | 1358 | What makes a good conversation? How controllable attributes affect human judgments | A good conversation requires balance -between simplicity and detail; staying on topic and changing it; asking questions and answering them. Although dialogue agents are commonly evaluated via human judgments of overall quality, the relationship between quality and these individual factors is less well-studied. In this ... | {
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Weighted Decoding (WD): During decoding, increase/decrease the probability of generating words w in proportion to features f(w). | Conditional Training (CT): Train the model to generate response y, conditioned on the input x, and the desired output attribute z.
Weighted Decoding (WD): During decoding, increase/decrease the probability of generating words w in proportion to features f(w). | [] |
GEM-SciDuet-train-133#paper-1358#slide-6 | 1358 | What makes a good conversation? How controllable attributes affect human judgments | A good conversation requires balance -between simplicity and detail; staying on topic and changing it; asking questions and answering them. Although dialogue agents are commonly evaluated via human judgments of overall quality, the relationship between quality and these individual factors is less well-studied. In this ... | {
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Conditional Training (CT): Weighted Decoding (WD):
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Ineffective at learning complex relationships between input and output ( response-relat... | [] |
GEM-SciDuet-train-133#paper-1358#slide-7 | 1358 | What makes a good conversation? How controllable attributes affect human judgments | A good conversation requires balance -between simplicity and detail; staying on topic and changing it; asking questions and answering them. Although dialogue agents are commonly evaluated via human judgments of overall quality, the relationship between quality and these individual factors is less well-studied. In this ... | {
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CT: Smaller range, but generally well- formed output | WD: Large range, but degenerate output at the extremes
CT: Smaller range, but generally well- formed output | [] |
GEM-SciDuet-train-133#paper-1358#slide-8 | 1358 | What makes a good conversation? How controllable attributes affect human judgments | A good conversation requires balance -between simplicity and detail; staying on topic and changing it; asking questions and answering them. Although dialogue agents are commonly evaluated via human judgments of overall quality, the relationship between quality and these individual factors is less well-studied. In this ... | {
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GEM-SciDuet-train-133#paper-1358#slide-9 | 1358 | What makes a good conversation? How controllable attributes affect human judgments | A good conversation requires balance -between simplicity and detail; staying on topic and changing it; asking questions and answering them. Although dialogue agents are commonly evaluated via human judgments of overall quality, the relationship between quality and these individual factors is less well-studied. In this ... | {
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(reduce genericness) to human level
Increase response- relatedness (similarity to last utterance) | Reduce n-gram repetition to human level
(reduce genericness) to human level
Increase response- relatedness (similarity to last utterance) | [] |
GEM-SciDuet-train-133#paper-1358#slide-10 | 1358 | What makes a good conversation? How controllable attributes affect human judgments | A good conversation requires balance -between simplicity and detail; staying on topic and changing it; asking questions and answering them. Although dialogue agents are commonly evaluated via human judgments of overall quality, the relationship between quality and these individual factors is less well-studied. In this ... | {
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Our raw engagingness score matches the
ConvAI2 competition winner's GPT-based model, even though ours is:
much smaller (2 layers vs 12) trained on 12x less data
However: On the humanness (i.e... | Yes! By controlling repetition, specificity and question-asking, we achieve near-human engagingness (i.e. enjoyability) ratings.
Our raw engagingness score matches the
ConvAI2 competition winner's GPT-based model, even though ours is:
much smaller (2 layers vs 12) trained on 12x less data
However: On the humanness (i.e... | [] |
GEM-SciDuet-train-133#paper-1358#slide-11 | 1358 | What makes a good conversation? How controllable attributes affect human judgments | A good conversation requires balance -between simplicity and detail; staying on topic and changing it; asking questions and answering them. Although dialogue agents are commonly evaluated via human judgments of overall quality, the relationship between quality and these individual factors is less well-studied. In this ... | {
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2. On this task, the human "engagingness" performance may be artificially low.
Turkers chatting for money are less engaging than people chatting for fun.
This may be why the human-level engagingness scores are easy to match. | Finding: Our bots are (almost) as engaging as humans, but they're clearly non-human.
2. On this task, the human "engagingness" performance may be artificially low.
Turkers chatting for money are less engaging than people chatting for fun.
This may be why the human-level engagingness scores are easy to match. | [] |
GEM-SciDuet-train-133#paper-1358#slide-12 | 1358 | What makes a good conversation? How controllable attributes affect human judgments | A good conversation requires balance -between simplicity and detail; staying on topic and changing it; asking questions and answering them. Although dialogue agents are commonly evaluated via human judgments of overall quality, the relationship between quality and these individual factors is less well-studied. In this ... | {
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Using simple control, we matched performance of GPT-based contest winner.
Don't repeat yourself. Don't be boring. Ask more questions.
Multi-turn phenomena (repetition, question-asking frequency) are important
so need multi-turn eval to detect t... | Control is a good idea for your neural sequence generation dialogue system.
Using simple control, we matched performance of GPT-based contest winner.
Don't repeat yourself. Don't be boring. Ask more questions.
Multi-turn phenomena (repetition, question-asking frequency) are important
so need multi-turn eval to detect t... | [] |
GEM-SciDuet-train-134#paper-1359#slide-0 | 1359 | Searching for the X-Factor: Exploring Corpus Subjectivity for Word Embeddings | We explore the notion of subjectivity, and hypothesize that word embeddings learnt from input corpora of varying levels of subjectivity behave differently on natural language processing tasks such as classifying a sentence by sentiment, subjectivity, or topic. Through systematic comparative analyses, we establish this ... | {
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Unsupervised training: GloVe, Word2Vec
Words in similar context tend to have similar meaning
Words with similar meanings tend to be close in embedding space | Dense vectors of words
Unsupervised training: GloVe, Word2Vec
Words in similar context tend to have similar meaning
Words with similar meanings tend to be close in embedding space | [] |
GEM-SciDuet-train-134#paper-1359#slide-1 | 1359 | Searching for the X-Factor: Exploring Corpus Subjectivity for Word Embeddings | We explore the notion of subjectivity, and hypothesize that word embeddings learnt from input corpora of varying levels of subjectivity behave differently on natural language processing tasks such as classifying a sentence by sentiment, subjectivity, or topic. Through systematic comparative analyses, we establish this ... | {
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GEM-SciDuet-train-134#paper-1359#slide-3 | 1359 | Searching for the X-Factor: Exploring Corpus Subjectivity for Word Embeddings | We explore the notion of subjectivity, and hypothesize that word embeddings learnt from input corpora of varying levels of subjectivity behave differently on natural language processing tasks such as classifying a sentence by sentiment, subjectivity, or topic. Through systematic comparative analyses, we establish this ... | {
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GEM-SciDuet-train-134#paper-1359#slide-5 | 1359 | Searching for the X-Factor: Exploring Corpus Subjectivity for Word Embeddings | We explore the notion of subjectivity, and hypothesize that word embeddings learnt from input corpora of varying levels of subjectivity behave differently on natural language processing tasks such as classifying a sentence by sentiment, subjectivity, or topic. Through systematic comparative analyses, we establish this ... | {
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Amazon Reviews (24 categories) + Rotten Tomatoes Reviews
A very funny movie vs. One lousy movie
Subjectivity Classification (subjective vs. objective)
The story needs more dramatic meat vs. She's an artist
Topic Classification (in-topic vs. out-of-topic)
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Amazon Reviews (24 categories) + Rotten Tomatoes Reviews
A very funny movie vs. One lousy movie
Subjectivity Classification (subjective vs. objective)
The story needs more dramatic meat vs. She's an artist
Topic Classification (in-topic vs. out-of-topic)
Newsgroups Data... | [] |
GEM-SciDuet-train-134#paper-1359#slide-7 | 1359 | Searching for the X-Factor: Exploring Corpus Subjectivity for Word Embeddings | We explore the notion of subjectivity, and hypothesize that word embeddings learnt from input corpora of varying levels of subjectivity behave differently on natural language processing tasks such as classifying a sentence by sentiment, subjectivity, or topic. Through systematic comparative analyses, we establish this ... | {
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Binary logistic regression classifier
Sentence embedding = average of word embeddings
The same number of sentences and the same vocabulary when training embeddings | Cross-validation on balanced samples
Binary logistic regression classifier
Sentence embedding = average of word embeddings
The same number of sentences and the same vocabulary when training embeddings | [] |
GEM-SciDuet-train-134#paper-1359#slide-8 | 1359 | Searching for the X-Factor: Exploring Corpus Subjectivity for Word Embeddings | We explore the notion of subjectivity, and hypothesize that word embeddings learnt from input corpora of varying levels of subjectivity behave differently on natural language processing tasks such as classifying a sentence by sentiment, subjectivity, or topic. Through systematic comparative analyses, we establish this ... | {
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"Comparative Analyses of Subjective vs. Objective C... | GEM-SciDuet-train-134#paper-1359#slide-8 | Empirical Findings | SE and OE are very similar on objective tasks
SE understand sentiment words better than OE?
Subjectivity Classification Topic Classification Amazon Sentiment Rotten Tomatoes Sentiment
SentiVec does not affect objective classification tasks
Amazon Sentiment (average over 24 categories) Rotten Tomatoes Sentiment | SE and OE are very similar on objective tasks
SE understand sentiment words better than OE?
Subjectivity Classification Topic Classification Amazon Sentiment Rotten Tomatoes Sentiment
SentiVec does not affect objective classification tasks
Amazon Sentiment (average over 24 categories) Rotten Tomatoes Sentiment | [] |
GEM-SciDuet-train-134#paper-1359#slide-9 | 1359 | Searching for the X-Factor: Exploring Corpus Subjectivity for Word Embeddings | We explore the notion of subjectivity, and hypothesize that word embeddings learnt from input corpora of varying levels of subjectivity behave differently on natural language processing tasks such as classifying a sentence by sentiment, subjectivity, or topic. Through systematic comparative analyses, we establish this ... | {
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GEM-SciDuet-train-134#paper-1359#slide-11 | 1359 | Searching for the X-Factor: Exploring Corpus Subjectivity for Word Embeddings | We explore the notion of subjectivity, and hypothesize that word embeddings learnt from input corpora of varying levels of subjectivity behave differently on natural language processing tasks such as classifying a sentence by sentiment, subjectivity, or topic. Through systematic comparative analyses, we establish this ... | {
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GEM-SciDuet-train-134#paper-1359#slide-12 | 1359 | Searching for the X-Factor: Exploring Corpus Subjectivity for Word Embeddings | We explore the notion of subjectivity, and hypothesize that word embeddings learnt from input corpora of varying levels of subjectivity behave differently on natural language processing tasks such as classifying a sentence by sentiment, subjectivity, or topic. Through systematic comparative analyses, we establish this ... | {
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Negative: waste, junk, horrible, defective,
Positive: love, great, recommend, easy, | Predicts context words as in
Negative: waste, junk, horrible, defective,
Positive: love, great, recommend, easy, | [] |
GEM-SciDuet-train-134#paper-1359#slide-13 | 1359 | Searching for the X-Factor: Exploring Corpus Subjectivity for Word Embeddings | We explore the notion of subjectivity, and hypothesize that word embeddings learnt from input corpora of varying levels of subjectivity behave differently on natural language processing tasks such as classifying a sentence by sentiment, subjectivity, or topic. Through systematic comparative analyses, we establish this ... | {
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"Comparative Analyses of Subjective vs. Objective C... | GEM-SciDuet-train-134#paper-1359#slide-13 | Logistic SentiVec | This camera is good for high quality
good good (good, camera)
good = 1 good
Random Noise (good, frog) (good, duck) | This camera is good for high quality
good good (good, camera)
good = 1 good
Random Noise (good, frog) (good, duck) | [] |
GEM-SciDuet-train-134#paper-1359#slide-14 | 1359 | Searching for the X-Factor: Exploring Corpus Subjectivity for Word Embeddings | We explore the notion of subjectivity, and hypothesize that word embeddings learnt from input corpora of varying levels of subjectivity behave differently on natural language processing tasks such as classifying a sentence by sentiment, subjectivity, or topic. Through systematic comparative analyses, we establish this ... | {
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"Comparative Analyses of Subjective vs. Objective C... | GEM-SciDuet-train-134#paper-1359#slide-14 | Spherical SentiVec | Positive Words Negative Words | Positive Words Negative Words | [] |
GEM-SciDuet-train-134#paper-1359#slide-15 | 1359 | Searching for the X-Factor: Exploring Corpus Subjectivity for Word Embeddings | We explore the notion of subjectivity, and hypothesize that word embeddings learnt from input corpora of varying levels of subjectivity behave differently on natural language processing tasks such as classifying a sentence by sentiment, subjectivity, or topic. Through systematic comparative analyses, we establish this ... | {
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GEM-SciDuet-train-134#paper-1359#slide-16 | 1359 | Searching for the X-Factor: Exploring Corpus Subjectivity for Word Embeddings | We explore the notion of subjectivity, and hypothesize that word embeddings learnt from input corpora of varying levels of subjectivity behave differently on natural language processing tasks such as classifying a sentence by sentiment, subjectivity, or topic. Through systematic comparative analyses, we establish this ... | {
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SentiVec, a method for infusing lexical information into word embeddings
Sentiment-infused SentiVec embeddings space facilitate better sentiment-related similarity
Pre-trained Word Embeddings & Code: https://sentivec.preferred.ai/ | Explored effects of corpus subjectivity for word embeddings
SentiVec, a method for infusing lexical information into word embeddings
Sentiment-infused SentiVec embeddings space facilitate better sentiment-related similarity
Pre-trained Word Embeddings & Code: https://sentivec.preferred.ai/ | [] |
GEM-SciDuet-train-135#paper-1364#slide-0 | 1364 | Rumor Detection on Twitter with Tree-structured Recursive Neural Networks | Automatic rumor detection is technically very challenging. In this work, we try to learn discriminative features from tweets content by following their non-sequential propagation structure and generate more powerful representations for identifying different type of rumors. We propose two recursive neural models based o... | {
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"Standard Recursive Neu... | GEM-SciDuet-train-135#paper-1364#slide-0 | Introduction | unverified or deliberately false
How the fake news propagated?
people tend to stop spreading a rumor if it
Previous studies focused on text mining from sequential microblog streams, we
denial want to bridge the content semantics and | unverified or deliberately false
How the fake news propagated?
people tend to stop spreading a rumor if it
Previous studies focused on text mining from sequential microblog streams, we
denial want to bridge the content semantics and | [] |
GEM-SciDuet-train-135#paper-1364#slide-1 | 1364 | Rumor Detection on Twitter with Tree-structured Recursive Neural Networks | Automatic rumor detection is technically very challenging. In this work, we try to learn discriminative features from tweets content by following their non-sequential propagation structure and generate more powerful representations for identifying different type of rumors. We propose two recursive neural models based o... | {
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It is crucial to track and debunk rumors early to minimize their harmful effects.
Online fact-checking services have limited topical coverage and long delay.
Existing models use feature engineering over simplistic; or recently deep neural networks ignore propagation st... | We generally are not good at distinguishing rumors
It is crucial to track and debunk rumors early to minimize their harmful effects.
Online fact-checking services have limited topical coverage and long delay.
Existing models use feature engineering over simplistic; or recently deep neural networks ignore propagation st... | [] |
GEM-SciDuet-train-135#paper-1364#slide-2 | 1364 | Rumor Detection on Twitter with Tree-structured Recursive Neural Networks | Automatic rumor detection is technically very challenging. In this work, we try to learn discriminative features from tweets content by following their non-sequential propagation structure and generate more powerful representations for identifying different type of rumors. We propose two recursive neural models based o... | {
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(a) RNN-based model (b) Tree kernel-based model
IDEA: Combining the two models, leveraging propagation structure by representation learning algorithm
Why such model do better?
Polarity stances (a) False rumor (b) True rumor
A reply usually respond to... | Existing works: Consider post representation or propagation structure
(a) RNN-based model (b) Tree kernel-based model
IDEA: Combining the two models, leveraging propagation structure by representation learning algorithm
Why such model do better?
Polarity stances (a) False rumor (b) True rumor
A reply usually respond to... | [] |
GEM-SciDuet-train-135#paper-1364#slide-3 | 1364 | Rumor Detection on Twitter with Tree-structured Recursive Neural Networks | Automatic rumor detection is technically very challenging. In this work, we try to learn discriminative features from tweets content by following their non-sequential propagation structure and generate more powerful representations for identifying different type of rumors. We propose two recursive neural models based o... | {
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Propose two variants of RvNN models based on bottom-up and top-down tree structures, to generate better integrated representations for a claim by captu... | The first study that deeply integrates both structure and content semantics based on tree-structured recursive neural networks for detecting rumors from microblog posts
Propose two variants of RvNN models based on bottom-up and top-down tree structures, to generate better integrated representations for a claim by captu... | [] |
GEM-SciDuet-train-135#paper-1364#slide-4 | 1364 | Rumor Detection on Twitter with Tree-structured Recursive Neural Networks | Automatic rumor detection is technically very challenging. In this work, we try to learn discriminative features from tweets content by following their non-sequential propagation structure and generate more powerful representations for identifying different type of rumors. We propose two recursive neural models based o... | {
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"Standard Recursive Neu... | GEM-SciDuet-train-135#paper-1364#slide-4 | Related Work | Systems based on common sense and investigative journalism,
Learning-based models for rumor detection
Using handcrafted and temporal features: Liu et al. (2015), Ma et al.
Tree-kernel-based model: Without hand-
images segmentation (Socher et al, 2011) phrase representation from word vectors (Socher et al, 2012)
Sentime... | Systems based on common sense and investigative journalism,
Learning-based models for rumor detection
Using handcrafted and temporal features: Liu et al. (2015), Ma et al.
Tree-kernel-based model: Without hand-
images segmentation (Socher et al, 2011) phrase representation from word vectors (Socher et al, 2012)
Sentime... | [] |
GEM-SciDuet-train-135#paper-1364#slide-5 | 1364 | Rumor Detection on Twitter with Tree-structured Recursive Neural Networks | Automatic rumor detection is technically very challenging. In this work, we try to learn discriminative features from tweets content by following their non-sequential propagation structure and generate more powerful representations for identifying different type of rumors. We propose two recursive neural models based o... | {
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Task 1 finer-grained classification for each source post
false rumor, true rumor, non-rumor, unverified rumor
Tas... | Given a set of microblog posts R = {}, model each source tweet as a tree structure T = < , >, where each node provide the text content of each post. And is directed edges corresponding to response relation.
Task 1 finer-grained classification for each source post
false rumor, true rumor, non-rumor, unverified rumor
Tas... | [] |
GEM-SciDuet-train-135#paper-1364#slide-6 | 1364 | Rumor Detection on Twitter with Tree-structured Recursive Neural Networks | Automatic rumor detection is technically very challenging. In this work, we try to learn discriminative features from tweets content by following their non-sequential propagation structure and generate more powerful representations for identifying different type of rumors. We propose two recursive neural models based o... | {
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"Standard Recursive Neu... | GEM-SciDuet-train-135#paper-1364#slide-6 | Tweet Structure | Root tweet : #Walmart donates $10,000 to #DarrenWilson bottom-up tree fund to continue police racial profiling
1:30 Idc if they killed a mf foreal. Ima always shop with @Walmart. I'm
: NEED SOURCE. have a feeling this is just hearsay ... just bein honest
I agree. I have been hearing this all day but no source 1:12
: Ex... | Root tweet : #Walmart donates $10,000 to #DarrenWilson bottom-up tree fund to continue police racial profiling
1:30 Idc if they killed a mf foreal. Ima always shop with @Walmart. I'm
: NEED SOURCE. have a feeling this is just hearsay ... just bein honest
I agree. I have been hearing this all day but no source 1:12
: Ex... | [] |
GEM-SciDuet-train-135#paper-1364#slide-7 | 1364 | Rumor Detection on Twitter with Tree-structured Recursive Neural Networks | Automatic rumor detection is technically very challenging. In this work, we try to learn discriminative features from tweets content by following their non-sequential propagation structure and generate more powerful representations for identifying different type of rumors. We propose two recursive neural models based o... | {
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p: the feature vector of a parent node whose children are and computation is done recursively over all tree nodes | RvNN (tree-structured neural networks) utilize sentence parse trees: representation associated with each node of a parse tree is computed from its direct children, computed by
p: the feature vector of a parent node whose children are and computation is done recursively over all tree nodes | [] |
GEM-SciDuet-train-135#paper-1364#slide-8 | 1364 | Rumor Detection on Twitter with Tree-structured Recursive Neural Networks | Automatic rumor detection is technically very challenging. In this work, we try to learn discriminative features from tweets content by following their non-sequential propagation structure and generate more powerful representations for identifying different type of rumors. We propose two recursive neural models based o... | {
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Structure: recursively visit every node from the leaves at the bottom to the root at the top (a natural extension to the original RvNN
Intuition: local rumor indicative features are aggregated along different branches (e.g., sub... | Input: bottom-up tree (node: a post represented as a vector of words ) GRU equation at node
Structure: recursively visit every node from the leaves at the bottom to the root at the top (a natural extension to the original RvNN
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GEM-SciDuet-train-135#paper-1364#slide-9 | 1364 | Rumor Detection on Twitter with Tree-structured Recursive Neural Networks | Automatic rumor detection is technically very challenging. In this work, we try to learn discriminative features from tweets content by following their non-sequential propagation structure and generate more powerful representations for identifying different type of rumors. We propose two recursive neural models based o... | {
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Own input Parent node
Structure: recursively visit from the root node to its children until reaching all leaf nodes. (reverse Bottom-up RvNN)
Intuition: rumor-indicative features are aggregated along the propagation path (e.g., if a post agree with its parents stance... | Input: top-down tree GRU transition equation at node
Own input Parent node
Structure: recursively visit from the root node to its children until reaching all leaf nodes. (reverse Bottom-up RvNN)
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GEM-SciDuet-train-135#paper-1364#slide-10 | 1364 | Rumor Detection on Twitter with Tree-structured Recursive Neural Networks | Automatic rumor detection is technically very challenging. In this work, we try to learn discriminative features from tweets content by following their non-sequential propagation structure and generate more powerful representations for identifying different type of rumors. We propose two recursive neural models based o... | {
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Bottom-up RvNN: the state of root node (i.e., source tweet) can be regard as the representation of the whole tree (can be used for supervised classification).
Top-down RvNN: the representation of each path... | Comparison: both of the two RvNN models aim to capture the structural properties by recursively visiting all nodes
Bottom-up RvNN: the state of root node (i.e., source tweet) can be regard as the representation of the whole tree (can be used for supervised classification).
Top-down RvNN: the representation of each path... | [] |
GEM-SciDuet-train-135#paper-1364#slide-11 | 1364 | Rumor Detection on Twitter with Tree-structured Recursive Neural Networks | Automatic rumor detection is technically very challenging. In this work, we try to learn discriminative features from tweets content by following their non-sequential propagation structure and generate more powerful representations for identifying different type of rumors. We propose two recursive neural models based o... | {
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URL of the datasets: https://www.dropbox.com/s/0jhsfwep3ywvpca/rumdetect2017.zip?dl=0 | Use two reference Tree datasets:
URL of the datasets: https://www.dropbox.com/s/0jhsfwep3ywvpca/rumdetect2017.zip?dl=0 | [] |
GEM-SciDuet-train-135#paper-1364#slide-12 | 1364 | Rumor Detection on Twitter with Tree-structured Recursive Neural Networks | Automatic rumor detection is technically very challenging. In this work, we try to learn discriminative features from tweets content by following their non-sequential propagation structure and generate more powerful representations for identifying different type of rumors. We propose two recursive neural models based o... | {
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DTC: Twitter information credibility model using Decision
RFC: Random Forest Classifier using three parameters to fit the temporal tweets volume curve (Kwon et al., 2013)
SVM-TS: Linear SVM classifier using time... | DTR: Decision tree-based ranking model using enquiry phrases to identify trending rumors (Zhao et al., 2015)
DTC: Twitter information credibility model using Decision
RFC: Random Forest Classifier using three parameters to fit the temporal tweets volume curve (Kwon et al., 2013)
SVM-TS: Linear SVM classifier using time... | [] |
GEM-SciDuet-train-135#paper-1364#slide-13 | 1364 | Rumor Detection on Twitter with Tree-structured Recursive Neural Networks | Automatic rumor detection is technically very challenging. In this work, we try to learn discriminative features from tweets content by following their non-sequential propagation structure and generate more powerful representations for identifying different type of rumors. We propose two recursive neural models based o... | {
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TR: True Rumor; UR: Unverified Rumor;
user info NR vs others) | NR: Non-Rumor; FR: False Rumor;
TR: True Rumor; UR: Unverified Rumor;
user info NR vs others) | [] |
GEM-SciDuet-train-135#paper-1364#slide-14 | 1364 | Rumor Detection on Twitter with Tree-structured Recursive Neural Networks | Automatic rumor detection is technically very challenging. In this work, we try to learn discriminative features from tweets content by following their non-sequential propagation structure and generate more powerful representations for identifying different type of rumors. We propose two recursive neural models based o... | {
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TR: True Rumor; UR: Unverified Rumor;
GRU-RNN models without hand-crafted features | NR: Non-Rumor; FR: False Rumor;
TR: True Rumor; UR: Unverified Rumor;
GRU-RNN models without hand-crafted features | [] |
GEM-SciDuet-train-135#paper-1364#slide-15 | 1364 | Rumor Detection on Twitter with Tree-structured Recursive Neural Networks | Automatic rumor detection is technically very challenging. In this work, we try to learn discriminative features from tweets content by following their non-sequential propagation structure and generate more powerful representations for identifying different type of rumors. We propose two recursive neural models based o... | {
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RvNN only need around
8 hours or about 90 tweets to achieve the comparable performance of the best baseline model. | In the first few hours, the accuracy of the RvNN- based methods climbs more rapidly and stabilize more quickly
RvNN only need around
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GEM-SciDuet-train-135#paper-1364#slide-16 | 1364 | Rumor Detection on Twitter with Tree-structured Recursive Neural Networks | Automatic rumor detection is technically very challenging. In this work, we try to learn discriminative features from tweets content by following their non-sequential propagation structure and generate more powerful representations for identifying different type of rumors. We propose two recursive neural models based o... | {
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Bottom-up RvNN: a set of responses supporting the parent posts that deny or question the source post.
Top-down RvNN: some patterns of propagation from the root to leaf nodes like supportdenysupport Baselines: sequential models may be con... | Example subtree of a rumor captured by the algorithm at early stage of propagation
Bottom-up RvNN: a set of responses supporting the parent posts that deny or question the source post.
Top-down RvNN: some patterns of propagation from the root to leaf nodes like supportdenysupport Baselines: sequential models may be con... | [] |
GEM-SciDuet-train-135#paper-1364#slide-17 | 1364 | Rumor Detection on Twitter with Tree-structured Recursive Neural Networks | Automatic rumor detection is technically very challenging. In this work, we try to learn discriminative features from tweets content by following their non-sequential propagation structure and generate more powerful representations for identifying different type of rumors. We propose two recursive neural models based o... | {
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"Standard Recursive Neu... | GEM-SciDuet-train-135#paper-1364#slide-17 | Conclusion and future work | Propose a bottom-up and a top-down tree-structured model based on recursive neural networks for rumor detection on Twitter.
Using propagation tree to guide the learning of representations from tweets content, such as embedding various indicative signals hidden in the structure, for better identifying rumors.
Results on... | Propose a bottom-up and a top-down tree-structured model based on recursive neural networks for rumor detection on Twitter.
Using propagation tree to guide the learning of representations from tweets content, such as embedding various indicative signals hidden in the structure, for better identifying rumors.
Results on... | [] |
GEM-SciDuet-train-136#paper-1365#slide-0 | 1365 | Robust Distant Supervision Relation Extraction via Deep Reinforcement Learning | Distant supervision has become the standard method for relation extraction. However, even though it is an efficient method, it does not come at no cost-The resulted distantly-supervised training samples are often very noisy. To combat the noise, most of the recent state-of-theart approaches focus on selecting onebest s... | {
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... | GEM-SciDuet-train-136#paper-1365#slide-0 | Relation Extraction | Plain Text Corpus Entity-Relation Triple Classifier
(Unstructured Info) (Structured Info) | Plain Text Corpus Entity-Relation Triple Classifier
(Unstructured Info) (Structured Info) | [] |
GEM-SciDuet-train-136#paper-1365#slide-1 | 1365 | Robust Distant Supervision Relation Extraction via Deep Reinforcement Learning | Distant supervision has become the standard method for relation extraction. However, even though it is an efficient method, it does not come at no cost-The resulted distantly-supervised training samples are often very noisy. To combat the noise, most of the recent state-of-theart approaches focus on selecting onebest s... | {
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... | GEM-SciDuet-train-136#paper-1365#slide-1 | Distant Supervision | If two entities participate in a relation, any sentence that contains those two entities might express that
Nijlen is a municipality located in the Belgian province of Antwerp.
Neural relation extraction with selective attention over instances. | If two entities participate in a relation, any sentence that contains those two entities might express that
Nijlen is a municipality located in the Belgian province of Antwerp.
Neural relation extraction with selective attention over instances. | [] |
GEM-SciDuet-train-136#paper-1365#slide-2 | 1365 | Robust Distant Supervision Relation Extraction via Deep Reinforcement Learning | Distant supervision has become the standard method for relation extraction. However, even though it is an efficient method, it does not come at no cost-The resulted distantly-supervised training samples are often very noisy. To combat the noise, most of the recent state-of-theart approaches focus on selecting onebest s... | {
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... | GEM-SciDuet-train-136#paper-1365#slide-2 | Wrong Labeling | Place_of_Death (William ODwyer, New York city)
i. Some New York city mayors William ODwyer, Vincent R. Impellitteri and Abraham Beame were born abroad.
Entity-Pair Level ii. Plenty of local officials have, too, including two New York city mayors,
Most of entity pairs only have several sentences | Place_of_Death (William ODwyer, New York city)
i. Some New York city mayors William ODwyer, Vincent R. Impellitteri and Abraham Beame were born abroad.
Entity-Pair Level ii. Plenty of local officials have, too, including two New York city mayors,
Most of entity pairs only have several sentences | [] |
GEM-SciDuet-train-136#paper-1365#slide-3 | 1365 | Robust Distant Supervision Relation Extraction via Deep Reinforcement Learning | Distant supervision has become the standard method for relation extraction. However, even though it is an efficient method, it does not come at no cost-The resulted distantly-supervised training samples are often very noisy. To combat the noise, most of the recent state-of-theart approaches focus on selecting onebest s... | {
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... | GEM-SciDuet-train-136#paper-1365#slide-3 | Requirements | General Purpose and Offline Process
Learn a Policy to Denoise the Training Data | General Purpose and Offline Process
Learn a Policy to Denoise the Training Data | [] |
GEM-SciDuet-train-136#paper-1365#slide-4 | 1365 | Robust Distant Supervision Relation Extraction via Deep Reinforcement Learning | Distant supervision has become the standard method for relation extraction. However, even though it is an efficient method, it does not come at no cost-The resulted distantly-supervised training samples are often very noisy. To combat the noise, most of the recent state-of-theart approaches focus on selecting onebest s... | {
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... | GEM-SciDuet-train-136#paper-1365#slide-4 | Deep Reinforcement Learning | The average vector of previous removed sentences
One relation type has an agent
Positive: Distantly-supervised positive sentences
Negative: Sampled from other relations
Split into training set and validation set
RL Agent da taset Train
RL Agent C leane d Train Relation Classifier d ataset | The average vector of previous removed sentences
One relation type has an agent
Positive: Distantly-supervised positive sentences
Negative: Sampled from other relations
Split into training set and validation set
RL Agent da taset Train
RL Agent C leane d Train Relation Classifier d ataset | [] |
GEM-SciDuet-train-136#paper-1365#slide-5 | 1365 | Robust Distant Supervision Relation Extraction via Deep Reinforcement Learning | Distant supervision has become the standard method for relation extraction. However, even though it is an efficient method, it does not come at no cost-The resulted distantly-supervised training samples are often very noisy. To combat the noise, most of the recent state-of-theart approaches focus on selecting onebest s... | {
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... | GEM-SciDuet-train-136#paper-1365#slide-5 | Reward | Positive Set Negative Set | Positive Set Negative Set | [] |
GEM-SciDuet-train-136#paper-1365#slide-6 | 1365 | Robust Distant Supervision Relation Extraction via Deep Reinforcement Learning | Distant supervision has become the standard method for relation extraction. However, even though it is an efficient method, it does not come at no cost-The resulted distantly-supervised training samples are often very noisy. To combat the noise, most of the recent state-of-theart approaches focus on selecting onebest s... | {
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... | GEM-SciDuet-train-136#paper-1365#slide-6 | Evaluation on a Synthetic Noise Dataset | False Positive: Other relation types
True Positive + False Positive: samples
False Positive Removed Part Epoch | False Positive: Other relation types
True Positive + False Positive: samples
False Positive Removed Part Epoch | [] |
GEM-SciDuet-train-136#paper-1365#slide-8 | 1365 | Robust Distant Supervision Relation Extraction via Deep Reinforcement Learning | Distant supervision has become the standard method for relation extraction. However, even though it is an efficient method, it does not come at no cost-The resulted distantly-supervised training samples are often very noisy. To combat the noise, most of the recent state-of-theart approaches focus on selecting onebest s... | {
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"Training Policy-based Agent",
... | GEM-SciDuet-train-136#paper-1365#slide-8 | Conclusion | We propose a deep reinforcement learning method
for robust distant supervision relation extraction.
Our method is model-agnostic.
Our method boost the performance of recently proposed neural relation extractors. | We propose a deep reinforcement learning method
for robust distant supervision relation extraction.
Our method is model-agnostic.
Our method boost the performance of recently proposed neural relation extractors. | [] |
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