Synonym Knowledge Enhanced Reader for Chinese Idiom Reading Comprehension
Siyu Long, Ran Wang, Kun Tao, Jiali Zeng, Xin-Yu Dai

TL;DR
This paper introduces a novel Chinese idiom reading comprehension model that leverages synonym knowledge and graph neural networks to better understand idioms' meanings, achieving state-of-the-art results.
Contribution
It proposes a synonym knowledge enhanced reader that encodes idiom relationships via a graph attention network, addressing semantic-literal meaning gaps in Chinese idiom comprehension.
Findings
Achieves state-of-the-art performance on ChID dataset.
Shows that synonym relationships improve idiom understanding.
Validates the effectiveness of graph-based encoding for idiom semantics.
Abstract
Machine reading comprehension (MRC) is the task that asks a machine to answer questions based on a given context. For Chinese MRC, due to the non-literal and non-compositional semantic characteristics, Chinese idioms pose unique challenges for machines to understand. Previous studies tend to treat idioms separately without fully exploiting the relationship among them. In this paper, we first define the concept of literal meaning coverage to measure the consistency between semantics and literal meanings for Chinese idioms. With the definition, we prove that the literal meanings of many idioms are far from their semantics, and we also verify that the synonymic relationship can mitigate this inconsistency, which would be beneficial for idiom comprehension. Furthermore, to fully utilize the synonymic relationship, we propose the synonym knowledge enhanced reader. Specifically, for each…
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Taxonomy
TopicsNatural Language Processing Techniques · Machine Learning in Bioinformatics
