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 answering, 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 considering other levels of word representations, thus resulting in incorrect matching decisions for cases where two words with different meanings 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 levels 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.
|Title of host publication||SIGIR 2019 - Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval|
|Publisher||Association for Computing Machinery, Inc|
|Number of pages||4|
|Publication status||Published - 18 Jul 2019|
|Event||42nd International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2019 - Paris, France|
Duration: 21 Jul 2019 → 25 Jul 2019
|Name||SIGIR 2019 - Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval|
|Conference||42nd International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2019|
|Period||21/07/2019 → 25/07/2019|
Bibliographical noteFunding 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.
© 2019 Association for Computing Machinery.
Copyright 2020 Elsevier B.V., All rights reserved.
- Multi-level matching network
- Text matching
ASJC Scopus subject areas
- Information Systems
- Applied Mathematics