Synonym Knowledge Enhanced Reader for C hinese Idiom Reading Comprehension (2020)

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 idiom, we first construct a synonym graph according to the annotations from the high-quality synonym dictionary or the cosine similarity between the pre-trained idiom embeddings and then incorporate the graph attention network and gate mechanism to encode the graph. Experimental results on ChID, a large-scale Chinese idiom reading comprehension dataset, show that our model achieves state-of-the-art performance.

## introduction machine reading comprehension is the task that asks a machine to answer questions based on a given context. although various advanced neural models have been proposed in recent years @xcite . chinese reading comprehension is still a challenging task @xcite . one of the reasons is the existence of chinese idioms, also known as "chengyu", whose non-literal and non-compositional characteristics make them difficult for machines to understand @xcite . recently, some studies @xcite claimed that good idiom representations are essential for idiom comprehension. meanwhile, many methods have been proposed to obtain idiom representations, which can be divided into two categories: first, a straightforward way is to treat each chinese idiom as a single token and obtain its representation by training on a large-scale corpus @xcite . however, compared to common words, chinese idioms appear less frequently. these methods are likely not sufficient to learn good enough idiom representations @xcite ; second, since most chinese idioms consist of four chinese characters, another direct approach is to treat each chinese idiom as a regular multi-word expression. in this category, most studies combine the distributional word embeddings @xcite to learn representations of multi-word expressions. additionally, some methods try to enhance the representations by employing a more powerful compositionality function @xcite @xcite , integrating combination rules @xcite , or incorporating the general linguistic knowledge @xcite . by combining the word embeddings, 𝑣 ! candidate idiom 𝑣 " neighboring synonyms synonymic relationship 𝑒 !" figure 2: example of synonym graph. the green node is the candidate idiom and yellow nodes are the synonyms. the edges in the graph represent the synonymic relationship. based on the discovery above, we propose the synonym knowledge enhanced reader (sker) to fully utilize the synonymic relationship. concretely, for each idiom, according to the annotations from a high-quality synonym dictionary or the cosine similarity between the pre-trained idiom embeddings, we construct a synonym graph to represent the synonymic relationship and then incorporate the graph attention network and gate mechanism to encode the graph. we evaluate our model on chid, a largescale chinese idiom reading comprehension dataset, and experimental results show that it achieves stateof-the-art performance. our contributions are threefold: (1) we quantitatively prove the inconsistency between semantics and literal meanings of chinese idioms; (2) we verify the synonymic relationship among idioms can mitigate this inconsistency effectively; (3) we propose a novel model to fully utilize the relationship and achieve state-of-the-art performance in chid, a large-scale chinese idiom reading comprehension dataset. ## preliminary study of synonymic relationship in this section, we first define the concept of literal meaning coverage for chinese idioms. then quantitative analyses are conducted to present the inconsistency between literal meanings and semantics of chinese idioms and the effectiveness of synonymic relationship in mitigating this inconsistency. ## proposed method in this section, we first give a formulation of chinese idiom reading comprehension. then we introduce how to construct the synonym graph. finally, we propose the synonym knowledge enhanced reader (sker) with the synonym graph encoding. ## related work machine reading comprehension machine reading comprehension (mrc) tasks require a machine reader to answer questions with given contexts. recently, many high-quality and large-scale reading comprehension datasets for english @xcite @xcite have been constructed to facilitate research on this topic. in turn, many neural models have been proposed to tackle these mrc problems, which approach or even surpass humans @xcite @xcite . similarly, mrc datasets for chinese have also been constructed, such as cmrc2018 @xcite , dureader @xcite , c3 @xcite , etc. among them, chid, proposed by @xcite , is a cloze-style multiple-choice mrc dataset. it aims at chinese idioms, also known as "chengyu", whose non-literal and non-compositional semantic characteristics pose unique challenges for machines to understand. idiom representation good idiom representations are crucial for natural language understanding @xcite . recent works made noticeable efforts in improving the quality of idiom representations by either treating each idiom as a single token @xcite or regarding idioms as regular multi-word expressions @xcite @xcite . however, the former approaches require large-scale corpora, while the latter approaches demonstrate the essentiality of semantic compositionality @xcite , which many idioms do not have. notably, both approaches treat idioms separately and ignore the relationship among them. different from these studies, we utilize the synonymic relationship to obtain idiom representations. with quantitative experiments, we show that the synonymic relationship can help machines comprehend idioms better. graph neural network graph neural network (gnn) has got extensive attention recently since they grant more flexibility to model non-euclidean data @xcite @xcite . they have shown promising performance in a range of natural language processing tasks. our work is closely related to the graph attention networks (gats), proposed by @xcite . gats can dynamically aggregate useful graph information by assigning different weights to neighboring nodes or associated edges, which are suitable for reducing the noise on the graph. in this paper, we use gats to aggregate the synonymic relationship and instead the vanilla gats, we incorporate the gate mechanism to further reduce the impact of incorrect synonymic relationship. ## conclusion in this paper, we give a quantitative analysis to prove that the literal meanings of many chinese idioms are far from their semantics, and also verify that the synonymic relationship can mitigate this inconsistency, which is beneficial for chinese idiom reading comprehension. we propose the synonym knowledge enhanced reader to fully utilize the relationship. experimental results show that our model achieves state-of-the-art performance among different settings of chid, a large-scale chinese idiom reading comprehension dataset. similar to chinese idioms, the inconsistency exists in a number of other language elements, e.g., slangs in english, where the potential use of the synonymic relationship among them requires a further exploration.