Utterance-focusing multiway-matching network for dialogue-based multiple-choice machine reading comprehension

Dialogue-based multiple-choice machine reading comprehension (MRC) is one of most difficult and novel tasks because it requires more advanced reading comprehension skills, such as speaker’s intention analysis, non-extractive reasoning, commonsense knowledge. Previous models usually only compute atte...

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Bibliographic Details
Published in:Neurocomputing (Amsterdam) 2021-02, Vol.425, p.12-22
Main Authors: Gu, Yingjie, Gui, Xiaolin, Li, Defu
Format: Article
Language:English
Subjects:
Attention
Dialogue
Machine reading comprehension
Multiple choice
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Summary:Dialogue-based multiple-choice machine reading comprehension (MRC) is one of most difficult and novel tasks because it requires more advanced reading comprehension skills, such as speaker’s intention analysis, non-extractive reasoning, commonsense knowledge. Previous models usually only compute attention scores from the fixed representation of entire dialogue, and also do not fully consider the contribution of dialogue, question, options, and their combinations respectively. In this paper, we introduce Utterance-focusing Multiway-matching Network (UMN), a simple but effective human mimicking model for dialogue-based multiple-choice MRC. First, two utterance-focusing mechanisms called ParaUF and AutoUF are proposed to extract the utterances that are most relevant to the question and option: ParaUF gets the bilinear weighted distance between each utterance of dialogue and question and option during training while AutoUF obtains the scores by the relevance, overlap and coverage (ROC) rules before training process. Second, we adopted the multiway-matching mechanism to capture the relationship among the question, option and selected utterances through calculating the attention weights between the quadruplet of four sequences: utterances, question, option and the concatenation of each two. We evaluate the proposed model on dialogue-based multiple-choice MRC tasks, DREAM, and outperformed recently published methods under the same pre-trained model. A series of detailed analysis is also conducted to interpret the differences of two utterance-focusing mechanisms and the effectiveness of the proposed multiway-matching mechanism.
ISSN:0925-2312
1872-8286
DOI:10.1016/j.neucom.2020.10.107