Improving Question Answering Model Robustness with Synthetic Adversarial Data Generation
Paper • 2104.08678 • Published
Model Overview
This is a RoBERTa-Large QA Model trained from https://huggingface.co/roberta-large in two stages. First, it is trained on synthetic adversarial data generated using a BART-Large question generator on Wikipedia passages from SQuAD, and then it is trained on SQuAD and AdversarialQA (https://arxiv.org/abs/2002.00293) in a second stage of fine-tuning.
Data
Training data: SQuAD + AdversarialQA Evaluation data: SQuAD + AdversarialQA
Training Process
Approx. 1 training epoch on the synthetic data and 2 training epochs on the manually-curated data.
Additional Information
Please refer to https://arxiv.org/abs/2104.08678 for full details.
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Datasets used to train mbartolo/roberta-large-synqa
Papers for mbartolo/roberta-large-synqa
Evaluation results
- Exact Match on squadvalidation set self-reported89.653
- F1 on squadvalidation set self-reported94.817
- Exact Match on adversarial_qavalidation set self-reported55.333
- F1 on adversarial_qavalidation set self-reported66.746