Term Similarity-aware Extensive and Intensive Reading For Multiple Choice Question Answering

IF 0.9 Q4 COMPUTER SCIENCE, SOFTWARE ENGINEERING
Xue Li, Junjie Zhang, Junlong Ma
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引用次数: 0

Abstract

Multiple Choice Question Answering(MCQA) aims to automatically choose a correct answer from candidate options when given a passage and question. Existing approaches generally model attention mechanisms based on whole-passage information or manually tag key sentences for weakly supervised learning, which leads to the models focusing extensively on redundant information and costly manual annotation. In this paper, we consider evidence sentence extraction work in an unsupervised way to precisely pinpoint evidence sentences and minimize the impact of redundant information while avoiding costly manual annotations. Specifically, we propose a novel model called Term Similarity-aware Extensive and Intensive Reading(TS-EIR), which dynamically and automatically refines critical information by term similarity. In detail, it intelligently selects sentences more relevant to the question from the passage and deeply extracts features by enhanced graph convolutional neural network. We apply the proposed TS-EIR to a typical pre-trained language model, BERT, for encoding and evaluate it on the RACE and Dream benchmarks, which verify our model achieves substantial performance improvements over the current baseline.
MCQA (Multiple Choice Question answer)的目的是在给定一篇文章或一个问题时,从考生的选项中自动选择一个正确答案。对于弱监督学习,现有的方法通常基于全文信息或手动标记关键句子来建模注意机制,这导致模型广泛关注冗余信息和昂贵的手动注释。在本文中,我们考虑以一种无监督的方式提取证据句子,以精确地定位证据句子,并最小化冗余信息的影响,同时避免昂贵的人工注释。具体来说,我们提出了一个新的模型,称为术语相似度感知的泛读和精读(TS-EIR),该模型根据术语相似度动态自动地提炼关键信息。它从文章中智能地选择与问题更相关的句子,并通过增强的图卷积神经网络深度提取特征。我们将提出的TS-EIR应用到一个典型的预训练语言模型BERT上进行编码,并在RACE和Dream基准上对其进行评估,这验证了我们的模型在当前基线上实现了实质性的性能改进。
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来源期刊
Scalable Computing-Practice and Experience
Scalable Computing-Practice and Experience COMPUTER SCIENCE, SOFTWARE ENGINEERING-
CiteScore
2.00
自引率
0.00%
发文量
10
期刊介绍: The area of scalable computing has matured and reached a point where new issues and trends require a professional forum. SCPE will provide this avenue by publishing original refereed papers that address the present as well as the future of parallel and distributed computing. The journal will focus on algorithm development, implementation and execution on real-world parallel architectures, and application of parallel and distributed computing to the solution of real-life problems.
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