Neural Graph Matching Networks for Chinese Short Text Matching

Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 6152–6158 July 5 - 10, 2020. c⃝2020 Association for Computational Linguistics 6152 Neural Graph Matching Networks for Chinese Short Text Matching Lu Chen, Yanbin Zhao, Boer Lv, Lesheng Jin, Zhi Chen, Su Zhu, Kai Yu∗ MoE Key Lab of Artificial Intelligence, AI Institute, Shanghai Jiao Tong University SpeechLab, Department of Computer Science and Engineering Shanghai Jiao Tong University, Shanghai, China {chenlusz, zhaoyb, boerlv, King18817550378}@sjtu.edu.cn {zhenchi713, paul2204, kai.yu}@sjtu.edu.cn Abstract Chinese short text matching usually employs word sequences rather than character se- quences to get better performance. How- ever, Chinese word segmentation can be er- roneous, ambiguous or inconsistent, which consequently hurts the final matching perfor- mance. To address this problem, we propose neural graph matching networks, a novel sen- tence matching framework capable of dealing with multi-granular input information. Instead of a character sequence or a single word se- quence, paired word lattices formed from mul- tiple word segmentation hypotheses are used as input and the model learns a graph represen- tation according to an attentive graph match- ing mechanism. Experiments on two Chinese datasets show that our models outperform the state-of-the-art short text matching models. 1 Introduction Short text matching (STM) is a fundamental task of natural language processing (NLP). It is usually recognized as a paraphrase identification task or a sentence semantic matching task. Given a pair of sentences, a matching model is to predict their semantic similarity. It is widely used in question answer systems and dialogue systems (Gao et al., 2019; Yu et al., 2014). The recent years have seen advances in deep learning methods for text matching (Mueller and Thyagarajan, 2016; Gong et al., 2017; Chen et al., 2017; Lan and Xu, 2018). However, almost all of these models are initially proposed for English text matching. Applying them for Chinese text matching, we have two choices. One is to take Chinese characters as the input of models. An- other is first to segment each sentence into words, and then to take these words as input tokens. Al- though character-based models can overcome the ∗Kai Yu is the corresponding author. Figure 1: An example of the word segmentation and the corresponding word lattice problem of data sparsity to some degree (Li et al., 2019), the main drawback of these models is that explicit word information is not fully exploited, which can be potentially useful for semantic match- ing. However, word-based models often suffer some potential issues caused by word segmen- tation. As shown in Figure 1, the character se- quence “南京市长江大桥(South Capital City Long River Big Bridge)” has two different mean- ings with different word segmentation. The first one refers to a bridge (Segment-1, Segment-2), and the other refers to a person (Segment-3). The ambiguity may be eliminated with more context. Additionally, the segmentation granularity of dif- ferent tools is different. For example, “长江大 桥(Yangtze River Bridge)” in Segment-1 is divided into two words “长江(Yangtze River)” and “大 桥(Bridge)” in Segment-2. It has been …

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