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A Study on Improving End-to-End Neural Coreference Resolution

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Chinese Computational Linguistics and Natural Language Processing Based on Naturally Annotated Big Data (CCL 2018, NLP-NABD 2018)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11221))

Abstract

This paper studies the methods to improve end-to-end neural coreference resolution. First, we introduce a coreference cluster modification algorithm, which can help modify the coreference cluster to rule out the dissimilar mention in the cluster and reduce errors caused by the global inconsistence of coreference clusters. Additionally, we tune the model from two aspects to get more accurate coreference resolution results. On one hand, the simple scoring function is replaced with a feed-forward neural network when computing the head word scores for later attention mechanism which can help pick out the most important word. On the other hand, the maximum width of a mention is tuned. Our experimental results show that above methods improve the performance of coreference resolution effectively.

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Acknowledgements

This work was funded in part by Chinese Academy of Sciences President’s International Fellowship Initiative (Grant No. 2018VTA0008).

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Authors and Affiliations

  1. National Engineering Laboratory for Speech and Language Information Processing, University of Science and Technology of China, Hefei, China

    Jia-Chen Gu, Zhen-Hua Ling & Nitin Indurkhya

Authors
  1. Jia-Chen Gu
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  2. Zhen-Hua Ling
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  3. Nitin Indurkhya
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Corresponding author

Correspondence to Zhen-Hua Ling .

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Editors and Affiliations

  1. Tsinghua University, Beijing, China

    Maosong Sun

  2. Harbin Institute of Technology, Harbin, China

    Ting Liu

  3. Beijing University of Posts and Telecommunications, Beijing, China

    Xiaojie Wang

  4. Tsinghua University, Beijing, China

    Zhiyuan Liu

  5. Tsinghua University, Beijing, China

    Yang Liu

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Gu, JC., Ling, ZH., Indurkhya, N. (2018). A Study on Improving End-to-End Neural Coreference Resolution. In: Sun, M., Liu, T., Wang, X., Liu, Z., Liu, Y. (eds) Chinese Computational Linguistics and Natural Language Processing Based on Naturally Annotated Big Data. CCL NLP-NABD 2018 2018. Lecture Notes in Computer Science(), vol 11221. Springer, Cham. https://doi.org/10.1007/978-3-030-01716-3_14

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  • DOI: https://doi.org/10.1007/978-3-030-01716-3_14

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-01715-6

  • Online ISBN: 978-3-030-01716-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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