End to End Chinese Lexical Fusion Recognition with Sememe Knowledge

Proceedings of the 28th International Conference on Computational Linguistics, pages 2935–2946 Barcelona, Spain (Online), December 8-13, 2020 2935 End to End Chinese Lexical Fusion Recognition with Sememe Knowledge Yijiang Liu1 and Meishan Zhang2 and Donghong Ji1∗ 1Key Laboratory of Aerospace Information Security and Trusted Computing, Ministry of Education, School of Cyber Science and Engineering, Wuhan University, China 2School of New Media and Communication, Tianjin University, China {cslyj,dhji}@whu.edu.cn, mason.zms@gmail.com Abstract In this paper, we present Chinese lexical fusion recognition, a new task which could be regarded as one kind of coreference recognition. First, we introduce the task in detail, showing the relationship with coreference recognition and differences from the existing tasks. Second, we propose an end-to-end model for the task, handling mentions as well as coreference relationship jointly. The model exploits the state-of-the-art contextualized BERT representations as the encoder, and is further enhanced with the sememe knowledge from HowNet by graph attention networks. We manually annotate a benchmark dataset for the task and then conduct experiments on it. Results demonstrate that our final model is effective and competitive for the task. Detailed analysis is offered for comprehensively understanding the new task and our proposed model. 1 Introduction Coreference is one important topic in linguistics (Gordon and Hendrick, 1998; Pinillos, 2011), and coref- erence recognition has been extensively researched in the natural language processing (NLP) community (Ng and Cardie, 2002; Lee et al., 2017; Qi et al., 2012; Fei et al., 2019). There are different kinds of coreferences, such as pronoun anaphora and abbreviation (Mitkov, 1998; Mitkov, 1999; Mu˜noz and Montoyo, 2002; Choubey and Huang, 2018). Here we examine the phenomenon of Chinese lexical fusion, also called separable coreference (Chen and Ren, 2020), where two closely related words in a paragraph are united by a fusion form of the same meaning, and the fusion word can be seen as the coreference of the two separation words. Since the fusion words are always out-of-vocabulary (OOV) words in downstream paragraph-level tasks such as reading comprehension, summarization, machine translation and etc., which can hinder the overall understanding and lead to degraded performance, the new task can offer informative knowledge to these tasks. 卡纳瓦罗打破受访惯例，公开训练未接受 采访。 Cannavaro broke the convention of accepting interviews, did not accept an interview for public training. 首批医务人员昨日返杭，其余人员预计两周内回到 杭州。 The first medical personnel returned to Hangzhou yesterday, the others are expected to return to Hangzhou within two weeks. 央行宣布下调人民币贷款基准利率，这是央行近期进行的第二次降息调整。 The central bank announced to reduce the benchmark interest rate for RMB loans, which is the second time to reduce the interest rate recently. Table 1: Examples of Chinese lexical fusion. *Corresponding author. This work is licensed under a Creative Commons Attribution 4.0 International Licence. Licence details: http:// creativecommons.org/licenses/by/4.0/. 2936 Table 1 shows three examples of Chinese lexical fusion. In the examples, “受访” (accept an interview), “返杭” (returned to Hangzhou) and “降息” (reduce the interest rate) are the fusion words, and their coref- erences are “接受 (accept)/采访 (interview)”, “回到 (return)/杭州 (Hangzhou)”, and “下调 (reduce)/利 率 (interest rate)”, respectively. Each corresponding of the fusion word …

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