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Deep Reinforcement Learning for Chinese Zero Pronoun Resolution

Summary

Chinese often omits pronouns, leaving "zero pronouns" whose referents must be recovered from context. Earlier neural resolvers score each pair of a zero pronoun and one candidate antecedent in isolation. They therefore ignore what earlier decisions reveal about later ones. Yin and colleagues recast anaphoric zero pronoun resolution as a Markov decision process. A policy-network agent walks through the candidate noun phrases one by one and decides whether each one is an antecedent. At every step, the agent's state includes a pooled summary of the candidates it has already accepted. This lets earlier choices inform later ones, such as rejecting a nearby mention that clashes with the entity already identified. The agent is pre-trained with supervision and then optimized with a reward equal to the F-score of its selected antecedents. It therefore targets the evaluation metric directly rather than a per-decision loss. On OntoNotes 5.0, the model beats prior state-of-the-art systems. The authors present it as the first reinforcement learning approach to Chinese zero pronoun resolution.

Key points

  • Candidate selection: candidates are maximal or modifier noun phrases located at most two sentences from the zero pronoun. This follows the heuristic of Chen and Ng (2016) and Yin et al. (2017a,b).
  • Encoders: the zero pronoun is encoded with the ZP-centered neural network of Yin et al. (2017a), which uses two RNNs over the preceding and following context. Each candidate phrase is encoded by a left-to-right RNN over its content words.
  • State vector: each state combines four parts:
    • the zero pronoun vector,
    • the current candidate vector,
    • pooled information from previously predicted antecedents,
    • pairwise features.
  • Learning: the authors chose a stochastic policy network trained with SGD over a Deep Q-Network. Their reasons were to avoid getting stuck in intermediate states and to obtain a more interpretable policy. Training starts with supervised pre-training.
  • Robustness: across 38 random seeds, F-scores ranged from 56.5% to 57.5%, with a median of 57.1%.

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

16 papers of our library cite it (of the 140673 whose bibliographies we read); 3 of them have a page here, 13 not yet.

Cite

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.18653/v1/p18-1053 · arXiv 1806.03711

@inproceedings{yin2018deep,
  title = {{Deep Reinforcement Learning for Chinese Zero Pronoun Resolution}},
  author = {Yin, Qingyu and Zhang, Yu and Zhang, Wei-Nan and Liu, Ting and Wang, William Yang},
  booktitle = {Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  year = {2018},
  doi = {10.18653/v1/p18-1053},
  eprint = {1806.03711},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.18653/v1/p18-1053}
}

References

3 references, 1 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

  1. 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
  2. SRK Branavan, David Silver, and Regina Barzilay. 2012. Learning to win by reading manuals in a monte-carlo framework. Journal of Artificial Intelligence Research, 43:661–704. find
  3. Chen Chen and Vincent Ng. 2013. Chinese zero pronoun resolution: Some recent advances. In EMNLP, pages 1360–1365. \#\# Conclusion find our card

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