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        <title>Don’t Mess with Mister-in-Between: Improved Negative Search for Knowledge Graph Completion</title>
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    <abstract>The best methods for knowledge graph completion use a ‘dual-encoding’ framework, a form of neural model with a bottleneck that facilitates fast approximate search over a vast collection of candidates. These approaches are trained using contrastive learning to differentiate between known positive examples and sampled negative instances. The mechanism for sampling negatives to date has been very simple, driven by pragmatic engineering considerations (e.g., using mismatched instances from the same batch). We propose several novel means of finding more informative negatives, based on searching for candidates with high lexical overlaps, from the dual-encoder model and according to knowledge graph structures. Experimental results on four benchmarks show that our best single model improves consistently over previous methods and obtains new state-of-the-art performance, including the challenging large-scale Wikidata5M dataset. Combing different kinds of strategies through model ensembling results in a further performance boost.</abstract>
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%0 Conference Proceedings
%T Don’t Mess with Mister-in-Between: Improved Negative Search for Knowledge Graph Completion
%A Jiang, Fan
%A Drummond, Tom
%A Cohn, Trevor
%Y Vlachos, Andreas
%Y Augenstein, Isabelle
%S Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics
%D 2023
%8 May
%I Association for Computational Linguistics
%C Dubrovnik, Croatia
%F jiang-etal-2023-dont
%X The best methods for knowledge graph completion use a ‘dual-encoding’ framework, a form of neural model with a bottleneck that facilitates fast approximate search over a vast collection of candidates. These approaches are trained using contrastive learning to differentiate between known positive examples and sampled negative instances. The mechanism for sampling negatives to date has been very simple, driven by pragmatic engineering considerations (e.g., using mismatched instances from the same batch). We propose several novel means of finding more informative negatives, based on searching for candidates with high lexical overlaps, from the dual-encoder model and according to knowledge graph structures. Experimental results on four benchmarks show that our best single model improves consistently over previous methods and obtains new state-of-the-art performance, including the challenging large-scale Wikidata5M dataset. Combing different kinds of strategies through model ensembling results in a further performance boost.
%R 10.18653/v1/2023.eacl-main.133
%U https://aclanthology.org/2023.eacl-main.133
%U https://doi.org/10.18653/v1/2023.eacl-main.133
%P 1818-1832

Markdown (Informal)

[Don’t Mess with Mister-in-Between: Improved Negative Search for Knowledge Graph Completion](https://aclanthology.org/2023.eacl-main.133) (Jiang et al., EACL 2023)

• Don’t Mess with Mister-in-Between: Improved Negative Search for Knowledge Graph Completion (Jiang et al., EACL 2023)

ACL

• Fan Jiang, Tom Drummond, and Trevor Cohn. 2023. Don’t Mess with Mister-in-Between: Improved Negative Search for Knowledge Graph Completion. In Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics, pages 1818–1832, Dubrovnik, Croatia. Association for Computational Linguistics.