MGMSN: Multi-Granularity Matching Model Based on Siamese Neural Network

Wang, Xin; Yang, Huizhong

doi:10.3389/fbioe.2022.839586

Cited by 4 publications

(2 citation statements)

References 31 publications

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“…Yu et al [14] employed interactive information to model diverse text pairs across various tasks and languages. Wang et al [15] concurrently considered deep and shallow semantic similarity, as well as granularity at the lexical and character levels, enabling a deeper exploration of similarity information. Chang et al [16] proposed semantic similarity analysis through the fusion of words and phrases.…”

Section: Related Workmentioning

confidence: 99%

“…Zou et al [27] employed RoBERTa as the backbone model and employed a divide-and-conquer strategy by decomposing keywords and intents for textual semantic matching. Wang et al [15] achieved multi-granularity feature encoding using ChineseBERT. They also developed a context-aware interaction module to model interactive information between sentences and utilized a multi-perspective fusion mechanism to integrate local and interaction information, capturing rich features of questions and answers.…”

Section: Related Workmentioning

confidence: 99%

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A Sentence-Matching Model Based on Multi-Granularity Contextual Key Semantic Interaction

Li,

2024

Applied Sciences

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In the task of matching Chinese sentences, the key semantics within sentences and the deep interaction between them significantly affect the matching performance. However, previous studies mainly relied on shallow interactions based on a single semantic granularity, which left them vulnerable to interference from overlapping terms. It is particularly challenging to distinguish between positive and negative examples within datasets from the same thematic domain. This paper proposes a sentence-matching model that incorporates multi-granularity contextual key semantic interaction. The model combines multi-scale convolution and multi-level convolution to extract different levels of contextual semantic information at word, phrase, and sentence granularities. It employs multi-head self-attention and cross-attention mechanisms to align the key semantics between sentences. Furthermore, the model integrates the original, similarity, and dissimilarity information of sentences to establish deep semantic interaction. Experimental results on both open- and closed-domain datasets demonstrate that the proposed model outperforms existing baseline models in terms of matching performance. Additionally, the model achieves matching effectiveness comparable to large-scale pre-trained language models while utilizing a lightweight encoder.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%