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        <title>Dynamic Entity Representations in Neural Language Models</title>
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    <name type="personal">
        <namePart type="given">Yangfeng</namePart>
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        <namePart type="given">Chenhao</namePart>
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    <name type="personal">
        <namePart type="given">Yejin</namePart>
        <namePart type="family">Choi</namePart>
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            <title>Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing</title>
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    <abstract>Understanding a long document requires tracking how entities are introduced and evolve over time. We present a new type of language model, EntityNLM, that can explicitly model entities, dynamically update their representations, and contextually generate their mentions. Our model is generative and flexible; it can model an arbitrary number of entities in context while generating each entity mention at an arbitrary length. In addition, it can be used for several different tasks such as language modeling, coreference resolution, and entity prediction. Experimental results with all these tasks demonstrate that our model consistently outperforms strong baselines and prior work.</abstract>
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%0 Conference Proceedings
%T Dynamic Entity Representations in Neural Language Models
%A Ji, Yangfeng
%A Tan, Chenhao
%A Martschat, Sebastian
%A Choi, Yejin
%A Smith, Noah A.
%Y Palmer, Martha
%Y Hwa, Rebecca
%Y Riedel, Sebastian
%S Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing
%D 2017
%8 September
%I Association for Computational Linguistics
%C Copenhagen, Denmark
%F ji-etal-2017-dynamic
%X Understanding a long document requires tracking how entities are introduced and evolve over time. We present a new type of language model, EntityNLM, that can explicitly model entities, dynamically update their representations, and contextually generate their mentions. Our model is generative and flexible; it can model an arbitrary number of entities in context while generating each entity mention at an arbitrary length. In addition, it can be used for several different tasks such as language modeling, coreference resolution, and entity prediction. Experimental results with all these tasks demonstrate that our model consistently outperforms strong baselines and prior work.
%R 10.18653/v1/D17-1195
%U https://aclanthology.org/D17-1195
%U https://doi.org/10.18653/v1/D17-1195
%P 1830-1839

Markdown (Informal)

[Dynamic Entity Representations in Neural Language Models](https://aclanthology.org/D17-1195) (Ji et al., EMNLP 2017)

• Dynamic Entity Representations in Neural Language Models (Ji et al., EMNLP 2017)

ACL

• Yangfeng Ji, Chenhao Tan, Sebastian Martschat, Yejin Choi, and Noah A. Smith. 2017. Dynamic Entity Representations in Neural Language Models. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pages 1830–1839, Copenhagen, Denmark. Association for Computational Linguistics.