Improving Question Answering Model Robustness with Synthetic Adversarial Data Generation

• URL: http://arxiv.org/abs/2104.08678v1
• Date: Sun, 18 Apr 2021 02:00:06 GMT
• Title: Improving Question Answering Model Robustness with Synthetic Adversarial Data Generation
• Authors: Max Bartolo, Tristan Thrush, Robin Jia, Sebastian Riedel, Pontus Stenetorp, Douwe Kiela
• Abstract summary: State-of-the-art question answering models remain susceptible to a variety of adversarial attacks and are still far from obtaining human-level language understanding. One proposed way forward is dynamic adversarial data collection, in which a human annotator attempts to create examples for which a model-in-the-loop fails. In this work, we investigate several answer selection, question generation, and filtering methods that form a synthetic adversarial data generation pipeline. Models trained on both synthetic and human-generated data outperform models not trained on synthetic adversarial data, and obtain state-of-the-art results on the Adversarial
• Score: 41.9785159975426
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Despite the availability of very large datasets and pretrained models, state-of-the-art question answering models remain susceptible to a variety of adversarial attacks and are still far from obtaining human-level language understanding. One proposed way forward is dynamic adversarial data collection, in which a human annotator attempts to create examples for which a model-in-the-loop fails. However, this approach comes at a higher cost per sample and slower pace of annotation, as model-adversarial data requires more annotator effort to generate. In this work, we investigate several answer selection, question generation, and filtering methods that form a synthetic adversarial data generation pipeline that takes human-generated adversarial samples and unannotated text to create synthetic question-answer pairs. Models trained on both synthetic and human-generated data outperform models not trained on synthetic adversarial data, and obtain state-of-the-art results on the AdversarialQA dataset with overall performance gains of 3.7F1. Furthermore, we find that training on the synthetic adversarial data improves model generalisation across domains for non-adversarial data, demonstrating gains on 9 of the 12 datasets for MRQA. Lastly, we find that our models become considerably more difficult to beat by human adversaries, with a drop in macro-averaged validated model error rate from 17.6% to 8.8% when compared to non-augmented models.

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