dialog_id stringlengths 8 11 | turns listlengths 0 148 |
|---|---|
dialog_0 | [
{
"bigram_overlap_prev": 0,
"context_embedding": [
0.008054360747337001,
-0.006782273296266001,
-0.039280399680137,
0.019158001989126,
-0.025144657120108,
-0.041528377681970007,
0.007677401416003001,
0.051242556422948005,
-0.046899046748876,
0.0562... |
dialog_1 | [{"bigram_overlap_prev":0.0,"context_embedding":[-0.063880138099193,-0.060171354562044005,-0.0690642(...TRUNCATED) |
dialog_2 | [{"bigram_overlap_prev":0.0,"context_embedding":[-0.027676252648234003,-0.035795632749795005,-0.0074(...TRUNCATED) |
dialog_3 | [{"bigram_overlap_prev":0.0,"context_embedding":[-0.062701150774955,0.018132947385311002,0.037748605(...TRUNCATED) |
dialog_4 | [{"bigram_overlap_prev":0.0,"context_embedding":[-0.072174318134784,0.027580942958593,0.002018752042(...TRUNCATED) |
dialog_5 | [{"bigram_overlap_prev":0.0,"context_embedding":[-0.034870214760303005,-0.083525404334068,-0.0070838(...TRUNCATED) |
dialog_6 | [{"bigram_overlap_prev":0.0,"context_embedding":[-0.12999278306961,0.068574033677577,0.0044086128473(...TRUNCATED) |
dialog_7 | [{"bigram_overlap_prev":0.0,"context_embedding":[-0.018918476998806,-0.051815964281558005,0.09923247(...TRUNCATED) |
dialog_8 | [{"bigram_overlap_prev":0.0,"context_embedding":[-0.072673745453357,0.060628589242696006,0.004762195(...TRUNCATED) |
dialog_9 | [{"bigram_overlap_prev":0.0,"context_embedding":[-0.000023058901206240993,-0.025007300078868002,0.00(...TRUNCATED) |
Taskmaster-1 Enriched Dialog Dataset (Combined)
Overview
This dataset is a combined, enriched version of the self_dialog and woz_dialog splits from the Taskmaster-1 dataset. It consists of multi-turn, human-human and human-simulated conversations with systematic enhancements for machine learning workflows—especially dialog modeling, generation, and fine-grained evaluation.
All conversations are structured in a JSON format with consistent schema and include added semantic, linguistic, and behavioral annotations.
Enrichments Included
- Role Embedding
Each turn includes a binary role embedding:
[1, 0] for USER
[0, 1] for ASSISTANT
This makes it easier for sequence models to learn speaker turns without relying on string labels.
Use case: Improves model performance in transformer-based dialog agents by allowing role-aware generation and classification.
- Response Candidates
Each user turn is enriched with nb_response_candidates — 2 to 4 plausible assistant responses sampled from the dataset. These are not ground truth but plausible continuations.
Use case: Ideal for retrieval-based dialog training or negative sampling in response ranking tasks.
- Readability Score
Computed using Flesch-Kincaid metrics and other NLP readability formulas. Stored as readability (0–100 scale, higher = easier).
Use case: Enables analysis of language complexity and training adaptive LLMs for education, accessibility, or voice interfaces.
- Readability Grade Score
Stored as readability_score on a U.S. grade level (lower = easier to read). Especially relevant for UX tuning.
Use case: Allows controlling reading level in generation tasks or selecting user-appropriate training samples.
- Context Embedding
Each turn is augmented with a context_embedding vector (384-dim, Sentence-BERT). Represents the semantic context of the turn.
Use case: Enables plug-and-play use with FAISS-based semantic search, response reranking, and memory-augmented generation.
- Speaker Role Flags
An is_user flag is included for each turn (1 = user, 0 = assistant).
Use case: Simplifies filtering, evaluation, or role-specific metric computation.
- Utterance Length Bucketing
Each turn is labeled as:
short (<= 5 tokens)
medium (6–15 tokens)
long (> 15 tokens)
Use case: Enables sampling, curriculum learning, or model analysis across turn complexity.
- Bigram Overlap with Previous Turn
Computed as bigram_overlap_prev (float between 0 and 1). Measures lexical repetition with the preceding utterance.
Use case: Useful for:
Dialogue coherence metrics
Detecting stagnation or repetition in generated responses
Analyzing repair-based utterances
- Sentiment Polarity
Computed using a sentiment analyzer. Stored as sentiment_polarity:
Ranges from –1 (strongly negative) to +1 (strongly positive)
Use case: Enables emotion-aware generation, tone control, or training sentiment-conditioned agents.
- Format Summary
Each conversation has:
dialog_id: Unique identifier
turns: List of enriched utterances
Each turn includes:
{ "speaker": "USER", "text": "I’d like to book a table for 2", "role_embedding": [1, 0], "intent_label": "request", "nb_response_candidates": [...], "readability_score": 4.5, "context_embedding": [...], "readability": 85.6, "is_user": 1, "length_bucket": "medium", "bigram_overlap_prev": 0.2, "sentiment_polarity": 0.1 }
Suggested Use Cases
Fine-tuning LLMs for goal-oriented dialog
Training dialog state trackers and response rankers
Evaluating model outputs with context-aware metrics
Curriculum learning based on length or readability
Emotion- and intent-conditioned dialog modeling
Semantic retrieval and reranking systems
Citation
@inproceedings{48484, title = {Taskmaster-1: Toward a Realistic and Diverse Dialog Dataset}, author = {Bill Byrne and Karthik Krishnamoorthi and Chinnadhurai Sankar and Arvind Neelakantan and Daniel Duckworth and Semih Yavuz and Ben Goodrich and Amit Dubey and Kyu-Young Kim and Andy Cedilnik}, year = {2019} }
Taskmaster-1: Towards a Realistic Goal-Oriented Dialogue Dataset (Google-Research-Datasets)
Original base dataset: @patil-suraj (Original contributor)
Enrichments and combined version by: GenAIDevTOProd (Adithya)
License: Same as Taskmaster-1 (if public domain or open license)
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