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        <title>Fast End-to-end Coreference Resolution for Korean</title>
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        <namePart type="given">Cheoneum</namePart>
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        <namePart type="given">Jamin</namePart>
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    <name type="personal">
        <namePart type="given">Sungjoon</namePart>
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    <name type="personal">
        <namePart type="given">Joonho</namePart>
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    <name type="personal">
        <namePart type="given">Changki</namePart>
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    <abstract>Recently, end-to-end neural network-based approaches have shown significant improvements over traditional pipeline-based models in English coreference resolution. However, such advancements came at a cost of computational complexity and recent works have not focused on tackling this problem. Hence, in this paper, to cope with this issue, we propose BERT-SRU-based Pointer Networks that leverages the linguistic property of head-final languages. Applying this model to the Korean coreference resolution, we significantly reduce the coreference linking search space. Combining this with Ensemble Knowledge Distillation, we maintain state-of-the-art performance 66.9% of CoNLL F1 on ETRI test set while achieving 2x speedup (30 doc/sec) in document processing time.</abstract>
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    <identifier type="doi">10.18653/v1/2020.findings-emnlp.237</identifier>
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        <date>2020-11</date>
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%0 Conference Proceedings
%T Fast End-to-end Coreference Resolution for Korean
%A Park, Cheoneum
%A Shin, Jamin
%A Park, Sungjoon
%A Lim, Joonho
%A Lee, Changki
%Y Cohn, Trevor
%Y He, Yulan
%Y Liu, Yang
%S Findings of the Association for Computational Linguistics: EMNLP 2020
%D 2020
%8 November
%I Association for Computational Linguistics
%C Online
%F park-etal-2020-fast
%X Recently, end-to-end neural network-based approaches have shown significant improvements over traditional pipeline-based models in English coreference resolution. However, such advancements came at a cost of computational complexity and recent works have not focused on tackling this problem. Hence, in this paper, to cope with this issue, we propose BERT-SRU-based Pointer Networks that leverages the linguistic property of head-final languages. Applying this model to the Korean coreference resolution, we significantly reduce the coreference linking search space. Combining this with Ensemble Knowledge Distillation, we maintain state-of-the-art performance 66.9% of CoNLL F1 on ETRI test set while achieving 2x speedup (30 doc/sec) in document processing time.
%R 10.18653/v1/2020.findings-emnlp.237
%U https://aclanthology.org/2020.findings-emnlp.237
%U https://doi.org/10.18653/v1/2020.findings-emnlp.237
%P 2610-2624

Markdown (Informal)

[Fast End-to-end Coreference Resolution for Korean](https://aclanthology.org/2020.findings-emnlp.237) (Park et al., Findings 2020)

• Fast End-to-end Coreference Resolution for Korean (Park et al., Findings 2020)

ACL

• Cheoneum Park, Jamin Shin, Sungjoon Park, Joonho Lim, and Changki Lee. 2020. Fast End-to-end Coreference Resolution for Korean. In Findings of the Association for Computational Linguistics: EMNLP 2020, pages 2610–2624, Online. Association for Computational Linguistics.