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MKPM: Multi keyword-pair matching for natural language sentences

Lu, Xin and Deng, Yao and Sun, Ting and Gao, Yi and Feng, Jun and Sun, Xia and Sutcliffe, Richard (2021) 'MKPM: Multi keyword-pair matching for natural language sentences.' Applied Intelligence. ISSN 0924-669X

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Sentence matching is widely used in various natural language tasks, such as natural language inference, paraphrase identification and question answering. For these tasks, we need to understand the logical and semantic relationship between two sentences. Most current methods use all information within a sentence to build a model and hence determine its relationship to another sentence. However, the information contained in some sentences may cause redundancy or introduce noise, impeding the performance of the model. Therefore, we propose a sentence matching method based on multi keyword-pair matching (MKPM), which uses keyword pairs in two sentences to represent the semantic relationship between them, avoiding the interference of redundancy and noise. Specifically, we first propose a sentence-pair-based attention mechanism <jats:italic>sp-attention</jats:italic> to select the most important word pair from the two sentences as a keyword pair, and then propose a Bi-task architecture to model the semantic information of these keyword pairs. The Bi-task architecture is as follows: 1. In order to understand the semantic relationship at the word level between two sentences, we design a word-pair task (WP-Task), which uses these keyword pairs to complete sentence matching independently. 2. We design a sentence-pair task (SP-Task) to understand the sentence level semantic relationship between the two sentences by sentence denoising. Through the integration of the two tasks, our model can understand sentences more accurately from the two granularities of word and sentence. Experimental results show that our model can achieve state-of-the-art performance in several tasks. Our source code is publicly available

Item Type: Article
Uncontrolled Keywords: Sentence matching, Multi keyword-pair, Bi-task architecture
Divisions: Faculty of Science and Health > Computer Science and Electronic Engineering, School of
Depositing User: Elements
Date Deposited: 22 Jun 2021 08:43
Last Modified: 22 Jun 2021 09:15

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