Transfer Learning Enhanced Single-choice Decision for Multi-choice Question Answering
This work addresses the problem of limited task-specific data in multi-choice machine reading comprehension for AI researchers, offering an incremental improvement by leveraging external resources.
The paper tackles multi-choice question answering by reformulating it as a binary classification task for each answer option, enabling transfer learning from other reading comprehension tasks. The method, based on ALBERT-xxlarge, achieves state-of-the-art results on RACE and DREAM datasets.
Multi-choice Machine Reading Comprehension (MMRC) aims to select the correct answer from a set of options based on a given passage and question. The existing methods employ the pre-trained language model as the encoder, share and transfer knowledge through fine-tuning.These methods mainly focus on the design of exquisite mechanisms to effectively capture the relationships among the triplet of passage, question and answers. It is non-trivial but ignored to transfer knowledge from other MRC tasks such as SQuAD due to task specific of MMRC.In this paper, we reconstruct multi-choice to single-choice by training a binary classification to distinguish whether a certain answer is correct. Then select the option with the highest confidence score as the final answer. Our proposed method gets rid of the multi-choice framework and can leverage resources of other tasks. We construct our model based on the ALBERT-xxlarge model and evaluate it on the RACE and DREAM datasets. Experimental results show that our model performs better than multi-choice methods. In addition, by transferring knowledge from other kinds of MRC tasks, our model achieves state-of-the-art results in both single and ensemble settings.