Adversarial Augmentation Policy Search for Domain and Cross-Lingual Generalization in Reading Comprehension
This work addresses robustness and generalization issues in reading comprehension models, which is important for NLP applications, but it is incremental as it builds on existing adversarial training and policy search methods.
The authors tackled the problem of reading comprehension models overfitting to training data and failing adversarial evaluation by proposing new adversarial generation methods and automated policy search for data augmentation. They demonstrated that using learned augmentation policies significantly improves model generalization across in-domain, out-of-domain, and cross-lingual settings, with concrete gains in languages like German, Russian, and Turkish.
Reading comprehension models often overfit to nuances of training datasets and fail at adversarial evaluation. Training with adversarially augmented dataset improves robustness against those adversarial attacks but hurts generalization of the models. In this work, we present several effective adversaries and automated data augmentation policy search methods with the goal of making reading comprehension models more robust to adversarial evaluation, but also improving generalization to the source domain as well as new domains and languages. We first propose three new methods for generating QA adversaries, that introduce multiple points of confusion within the context, show dependence on insertion location of the distractor, and reveal the compounding effect of mixing adversarial strategies with syntactic and semantic paraphrasing methods. Next, we find that augmenting the training datasets with uniformly sampled adversaries improves robustness to the adversarial attacks but leads to decline in performance on the original unaugmented dataset. We address this issue via RL and more efficient Bayesian policy search methods for automatically learning the best augmentation policy combinations of the transformation probability for each adversary in a large search space. Using these learned policies, we show that adversarial training can lead to significant improvements in in-domain, out-of-domain, and cross-lingual (German, Russian, Turkish) generalization.