Transfer Learning Enhanced Single-choice Decision for Multi-choice Question Answering

• URL: http://arxiv.org/abs/2404.17949v1
• Date: Sat, 27 Apr 2024 16:02:55 GMT
• Title: Transfer Learning Enhanced Single-choice Decision for Multi-choice Question Answering
• Authors: Chenhao Cui, Yufan Jiang, Shuangzhi Wu, Zhoujun Li,
• Abstract summary: Multi-choice Machine Reading (MMRC) aims to select the correct answer from a set of options based on a given passage and question. In this paper, we reconstruct multi-choice to single-choice by training a binary classification to distinguish whether a certain answer is correct. Our proposed method gets rid of the multi-choice framework and can leverage resources of other tasks.
• Score: 27.601353412882258
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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