Chinese sentence semantic matching based on multi-level relevance extraction and aggregation for intelligent human–robot interaction

Wenpeng Lu*, Pengyu Zhao, Yifeng Li, Shoujin Wang, Heyan Huang, Shumin Shi, Hao Wu

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)

Abstract

With the development of Internet of Things and cloud computing, intelligent question-answering (QA) has brought great convenience to human's daily activities. As one of the core technologies, sentence semantic matching (SSM) plays a critical role in a variety of intelligent QA systems. However, existing SSM methods usually first encode sentences on either character or word level, and then model semantic interactions on sentence level. Consequently, they fail to capture the rich interactions on multi-levels (i.e., character, word and sentence levels). In this paper, we propose Chinese sentence semantic matching based on Multi-level Relevance Extraction and Aggregation (MREA) for intelligent QA. MREA can comprehensively capture and aggregate various semantic relevance on character, word and sentence levels respectively based on multiple attention mechanisms. Extensive experiments on two real-world datasets demonstrate that MREA outperforms the best-performing baselines by 0.5% and 0.89% w.r.t. ACC. and F1 respectively, and achieves comparable performance with BERT-based methods.

Original languageEnglish
Article number109795
Pages (from-to)1-11
Number of pages11
JournalApplied Soft Computing
Volume131
DOIs
Publication statusPublished - Dec 2022
Externally publishedYes

Keywords

  • Sentence semantic matching
  • Human–robot interface
  • Attention mechanism
  • Feature extraction and aggregation

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