Multi-Level Matching Networks for Text Matching

Chunlin Xu, Zhiwei Lin, Hui Wang, Shengli Wu

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Citations (Scopus)
21 Downloads (Pure)

Abstract

Text matching aims to establish the matching relationship between two texts. It is an important operation in some information retrieval related tasks such as question duplicate detection, question answer- ing, and dialog systems. Bidirectional long short term memory (BiLSTM) coupled with attention mechanism has achieved state-of- the-art performance in text matching. A major limitation of existing works is that only high level contextualized word representations are utilized to obtain word level matching results without consider- ing other levels of word representations, thus resulting in incorrect matching decisions for cases where two words with different mean- ings are very close in high level contextualized word representation space. Therefore, instead of making decisions utilizing single level word representations, a multi-level matching network (MMN) is proposed in this paper for text matching, which utilizes multiple lev- els of word representations to obtain multiple word level matching results for final text level matching decision. Experimental results on two widely used benchmarks, SNLI and Scaitail, show that the proposed MMN achieves the state-of-the-art performance.
Original languageEnglish
Title of host publicationSIGIR 2019 - Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval
PublisherAssociation for Computing Machinery
Pages949-952
Number of pages4
ISBN (Electronic)9781450361729
ISBN (Print)978-1-4503-6172-9
DOIs
Publication statusPublished - 18 Jul 2019

Publication series

NameProceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval
PublisherAssociation for Computing Machinery

Bibliographical note

This has an ISBN

Funding Information: This work is partially funded by the EU Horizon 2020 under Grant 690238 for DESIREE Project, under Grant 700381 for ASGARD project, by the UK EPSRC under Grant EP/P031668/1. Publisher Copyright: © 2019 Association for Computing Machinery. Copyright: Copyright 2019 Elsevier B.V., All rights reserved

Keywords

  • Attention
  • Multi-level matching network
  • Text matching
  • text matching
  • attention
  • multi-level matching network

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