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        <title>Incremental Neural Coreference Resolution in Constant Memory</title>
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    <name type="personal">
        <namePart type="given">Patrick</namePart>
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    <name type="personal">
        <namePart type="given">Benjamin</namePart>
        <namePart type="family">Van Durme</namePart>
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            <title>Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)</title>
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            <namePart type="given">Bonnie</namePart>
            <namePart type="family">Webber</namePart>
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        <name type="personal">
            <namePart type="given">Trevor</namePart>
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            <namePart type="given">Yang</namePart>
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    <abstract>We investigate modeling coreference resolution under a fixed memory constraint by extending an incremental clustering algorithm to utilize contextualized encoders and neural components. Given a new sentence, our end-to-end algorithm proposes and scores each mention span against explicit entity representations created from the earlier document context (if any). These spans are then used to update the entity‘s representations before being forgotten; we only retain a fixed set of salient entities throughout the document. In this work, we successfully convert a high-performing model (Joshi et al., 2020), asymptotically reducing its memory usage to constant space with only a 0.3% relative loss in F1 on OntoNotes 5.0.</abstract>
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    <identifier type="doi">10.18653/v1/2020.emnlp-main.695</identifier>
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        <date>2020-11</date>
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%0 Conference Proceedings
%T Incremental Neural Coreference Resolution in Constant Memory
%A Xia, Patrick
%A Sedoc, João
%A Van Durme, Benjamin
%Y Webber, Bonnie
%Y Cohn, Trevor
%Y He, Yulan
%Y Liu, Yang
%S Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
%D 2020
%8 November
%I Association for Computational Linguistics
%C Online
%F xia-etal-2020-incremental
%X We investigate modeling coreference resolution under a fixed memory constraint by extending an incremental clustering algorithm to utilize contextualized encoders and neural components. Given a new sentence, our end-to-end algorithm proposes and scores each mention span against explicit entity representations created from the earlier document context (if any). These spans are then used to update the entity‘s representations before being forgotten; we only retain a fixed set of salient entities throughout the document. In this work, we successfully convert a high-performing model (Joshi et al., 2020), asymptotically reducing its memory usage to constant space with only a 0.3% relative loss in F1 on OntoNotes 5.0.
%R 10.18653/v1/2020.emnlp-main.695
%U https://aclanthology.org/2020.emnlp-main.695/
%U https://doi.org/10.18653/v1/2020.emnlp-main.695
%P 8617-8624

Markdown (Informal)

[Incremental Neural Coreference Resolution in Constant Memory](https://aclanthology.org/2020.emnlp-main.695/) (Xia et al., EMNLP 2020)

• Incremental Neural Coreference Resolution in Constant Memory (Xia et al., EMNLP 2020)

ACL

• Patrick Xia, João Sedoc, and Benjamin Van Durme. 2020. Incremental Neural Coreference Resolution in Constant Memory. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 8617–8624, Online. Association for Computational Linguistics.