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Zero Pronoun Resolution with Attention-based Neural Network

Summary

The paper addresses anaphoric zero pronoun resolution in Chinese, a pro-drop language. A zero pronoun is an omitted argument with no surface form, so it must be represented through its context. Earlier neural approaches either used only a few nearby words or treated every context word as equally relevant.

The authors propose a pair-wise neural model with two components:

  • Zero pronoun encoder: a self-attentive mechanism, inspired by Lin et al. (2017), applies several attention hops over the words before and after the gap.
  • Candidate encoder: an attention-based RNN encodes each candidate noun phrase. It uses the zero pronoun's vector to weight the words inside the mention.

The two vectors pass through tanh layers, which output a coreference score for each pronoun–candidate pair.

On the Chinese portion of OntoNotes 5.0, the model outperforms prior systems, including the strongest baseline (Liu et al., 2017), and is reported as state of the art. The work matters because it shows that selectively attending to context and to mention words helps represent elements absent from the text. This idea may carry over to other pro-drop languages.

Key points

  • Candidate selection: noun phrases within two sentences of the gap, limited to max or modifier NPs. This retains about 98% of the true antecedents.
  • Per-genre gains over Liu et al. (2017) in F-score:
    • NW: +5.1
    • BN: +4.6
    • BC: +2.1
    • WB: +1.5
    • MZ: +1.2
    • TC: +0.3
  • Why TC lags: the gain on telephone conversation (TC) is marginal, which the authors attribute to short texts and many filler words.
  • Number of attention hops (r): r = 2 gives the best results among the values tried (2–6).
  • Ablation: removing attention entirely costs 0.9 F-score.
  • Training dynamics: on the development set, F-score rises from about 46% after the first epoch to about 52% after roughly 30 iterations, then levels off.
  • Case study: attention heat maps show the model focusing on informative words such as "数字证书/digital certificate".

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

Fields: Linguistics & languages

Topics: Coreference resolution

Cited by

15 papers of our library cite it (of the 140673 whose bibliographies we read); 6 of them have a page here, 9 not yet.

Cite

The paper itself, not this page. BibTeX from the library's record of the work.

@inproceedings{yin2018zero,
  title = {{Zero Pronoun Resolution with Attention-based Neural Network}},
  author = {Yin, Qingyu and Zhang, Yu and Zhang, Weinan and Liu, Ting and Wang, William Yang},
  booktitle = {Proceedings of the 27th International Conference on Computational Linguistics},
  year = {2018},
  url = {https://aclanthology.org/C18-1002/}
}

References

25 references, 14 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

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  2. Chen Chen and Vincent Ng. 2014. Chinese zero pronoun resolution: An unsupervised approach combining ranking and integer linear programming. In Twenty-Eighth AAAI Conference on Artificial Intelligence. find
  3. Chen Chen and Vincent Ng. 2015. Chinese zero pronoun resolution: A joint unsupervised discourse-aware model rivaling state-of-the-art resolvers. In Proceedings of the 53rd Annual Meeting of the ACL and the 7th International Joint Conference on Natural Language Processing (Volume 2: Short Papers), pa… find our card
  4. Chen Chen and Vincent Ng. 2016. Chinese zero pronoun resolution with deep neural networks. In Proceedings of the 54rd Annual Meeting of the ACL. find our card
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  16. Fang Kong and Guodong Zhou. 2010. A tree kernel-based unified framework for chinese zero anaphora resolution. In Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing, pages 882–891. Association for Computational Linguistics. find our card
  17. Zhouhan Lin, Minwei Feng, Cicero Nogueira dos Santos, Mo Yu, Bing Xiang, Bowen Zhou, and Yoshua Bengio. 2017. A structured self-attentive sentence embedding. arXiv preprint arXiv:1703.03130. find our card
  18. Ting Liu, Yiming Cui, Qingyu Yin, Shijin Wang, Weinan Zhang, and Guoping Hu. 2017. Generating and exploiting large-scale pseudo training data for zero pronoun resolution. In ACL. find our card
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