Multi-Task Learning with Generative Adversarial Training for Multi-Passage Machine Reading Comprehension
Multi-passage machine reading comprehension (MRC) aims to answer a question by multiple passages. Existing multi-passage MRC approaches have shown that employing passages with and without golden answers (i.e. labeled and unlabeled passages) for model training can improve prediction accuracy. In this paper, we present MG-MRC, a novel approach for multi-passage MRC via multi-task learning with generative adversarial training. MG-MRC adopts the extract-then-select framework, where an extractor is first used to predict answer candidates, then a selector is used to choose the final answer. In MG-MRC, we adopt multi-task learning to train the extractor by using both labeled and unlabeled passages. In particular, we use labeled passages to train the extractor by supervised learning, while using unlabeled passages to train the extractor by generative adversarial training, where the extractor is regarded as the generator and a discriminator is introduced to evaluate the generated answer candidates. Moreover, to train the extractor by backpropagation in the generative adversarial training process, we propose a hybrid method which combines boundary-based and content-based extracting methods to produce the answer candidate set and its representation. The experimental results on three open-domain QA datasets confirm the effectiveness of our approach.