The DOI matches its Crossref record; 57 of the 57 references it cites were found in a registry or an index.
Neural Natural Language Inference Models Enhanced with External Knowledge
Open accessAbstract
Modeling natural language inference is a very challenging task.With the availability of large annotated data, it has recently become feasible to train complex models such as neural-network-based inference models, which have shown to achieve the state-of-the-art performance.Although there exist relatively large annotated data, can machines learn all knowledge needed to perform natural language inference (NLI) from these data?If not, how can neural-network-based NLI models benefit from external knowledge and how to build NLI models to leverage it?In this paper, we enrich the state-of-the-art neural natural language inference models with external knowledge.We demonstrate that the proposed models improve neural NLI models to achieve the state-of-the-art performance on the SNLI and MultiNLI datasets.
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Cited by
Deep Learning--based Text Classification: A Comprehensive Review
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ERNIE: Enhanced Language Representation with Informative Entities
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Knowledge Enhanced Contextual Word Representations
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Breaking NLI Systems with Sentences that Require Simple Lexical Inferences
Glockner, M., Shwartz, V., & Goldberg, Y. (2018) · Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers) · doi:10.18653/v1/p18-2103 · Cited by 319
Metadata
- Authors
- Chen, Q., Zhu, X., Ling, Z.-H., Inkpen, D., & Wei, S.
- Venue
- Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
- Published
- 2018
- Pages
- 2406-2417
- OpenAlex
- W2798416089
- Cited by
- 253
- References
- 57
- FWCI
- 17.58
- Type
- conference paper
- Language
- en
- Open access
- hybrid
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- Chen, Q., Zhu, X., Ling, Z.-H., Inkpen, D., & Wei, S. (2018). Neural Natural Language Inference Models Enhanced with External Knowledge. Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). https://doi.org/10.18653/v1/p18-1224
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