#SPLIT AND CONQUER MODS#

#SPLIT AND CONQUER MODS#

Cite (Informal): Divide and Conquer: Text Semantic Matching with Disentangled Keywords and Intents (Zou et al., Findings 2022) Copy Citation: BibTeX Markdown MODS XML Endnote More options… PDF: Code rowitzou/dc-match Data GLUE, = 'Divide and Conquer: Text Semantic Matching with Disentangled Keywords and Intents',īooktitle = 'Findings of the Association for Computational Linguistics: ACL 2022', Association for Computational Linguistics. In Findings of the Association for Computational Linguistics: ACL 2022, pages 3622–3632, Dublin, Ireland. Divide and Conquer: Text Semantic Matching with Disentangled Keywords and Intents. Anthology ID: 2022.findings-acl.287 Volume: Findings of the Association for Computational Linguistics: ACL 2022 Month: May Year: 2022 Address: Dublin, Ireland Venue: Findings SIG: Publisher: Association for Computational Linguistics Note: Pages: 3622–3632 Language: URL: DOI: 10.18653/v1/2022.findings-acl.287 Bibkey: zou-etal-2022-divide Cite (ACL): Yicheng Zou, Hongwei Liu, Tao Gui, Junzhe Wang, Qi Zhang, Meng Tang, Haixiang Li, and Daniell Wang. Our approach can be easily combined with pre-trained language models (PLM) without influencing their inference efficiency, achieving stable performance improvements against a wide range of PLMs on three benchmarks. In this work, we propose a simple yet effective training strategy for text semantic matching in a divide-and-conquer manner by disentangling keywords from intents. Specifically, keywords represent factual information such as action, entity, and event that should be strictly matched, while intents convey abstract concepts and ideas that can be paraphrased into various expressions. However, a query sentence generally comprises content that calls for different levels of matching granularity. Most state-of-the-art matching models, e.g., BERT, directly perform text comparison by processing each word uniformly. Abstract Text semantic matching is a fundamental task that has been widely used in various scenarios, such as community question answering, information retrieval, and recommendation.

Split and conquer