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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2404.17949 |
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| _version_ | 1866911856795320320 |
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| author | Cui, Chenhao Jiang, Yufan Wu, Shuangzhi Li, Zhoujun |
| author_facet | Cui, Chenhao Jiang, Yufan Wu, Shuangzhi Li, Zhoujun |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_17949 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Transfer Learning Enhanced Single-choice Decision for Multi-choice Question Answering Cui, Chenhao Jiang, Yufan Wu, Shuangzhi Li, Zhoujun Computation and Language 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. |
| title | Transfer Learning Enhanced Single-choice Decision for Multi-choice Question Answering |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2404.17949 |