Chinese short text matching usually employs word sequences rather than character sequences to get better performance . However , Chinese word segmentation can be erroneous , ambiguous or inconsistent , which consequently hurts the final matching performance . To address this problem , we propose neural graph matching networks , a novel sentence matching framework capable of dealing with multi-granular input information . Instead of a character sequence or a single word sequence , paired word lattices formed from multiple word segmentation hypotheses are used as input and the model learns a graph representation according to an attentive graph matching mechanism . Experiments on two Chinese datasets show that our models outperform the state-of-the-art short text matching models . 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 . Another is first to segment each sentence into words , and then to take these words as input tokens . Although character-based models can overcome the * Kai Yu is the corresponding author . 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 matching . However , word-based models often suffer some potential issues caused by word segmentation . As shown in Figure 1 , the character sequence " 南 京 市 长 江 大 桥(South Capital City Long River Big Bridge ) " has two different meanings 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 different 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 shown that multi-granularity information is important for text matching ( Lai et al . , 2019 ) . Here we propose a neural graph matching method ( GMN ) for Chinese short text matching . Instead of segmenting each sentence into a word sequence , we keep all possible segmentation paths to form a word lattice graph , as shown in Figure 1 . GMN takes a pair of word lattice graphs as input and updates the representations of nodes according to the graph matching attention mechanism . Also , GMN can be combined with pre-trained language models , e.g. BERT ( Devlin et al . , 2019 ) . It can be regarded as a method to integrate word information in these pre-trained language models during the fine-tuning phase . The experiments on two Chinese Datasets show that our model outperforms not only previous state-of-the-art models but also the pre-trained model BERT as well as some variants of BERT . In this paper , we propose a neural graph matching model for Chinese short text matching . It takes a pair of word lattices as input instead of word or character sequences . The utilization of word lattice can provide more multi-granularity information and avoid the error propagation issue of word segmentation . Additionally , our model and the pre-training model are complementary . It can be regarded as a flexible method to introduce word information into BERT during the fine-tuning phase . The experimental results show that our model outperforms the state-of-the-art text matching models as well as some BERT-based models .

Challenge: Word-base solutions for text matching tasks in Chinese require segmenting texts into words which can be prone to error hurting the final performance.
Approach: They propose neural graph matching networks inspired which can take multiple word segmentation hypotheses into account using a word lattice graph.
Outcome: The proposed models outperform the state-of-the-art models and BERT-based models on two Chinese text matching datasets.