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ZPR2: Joint Zero Pronoun Recovery and Resolution using Multi-Task Learning and BERT

Summary

Chinese often omits pronouns, especially in conversation, which hurts machine translation, QA and dialogue systems. The authors tackle two related problems together: ZP recovery (restoring the omitted pronoun's form) and ZP resolution (finding the mention it refers to). Earlier work handled them separately and usually assumed gold parse trees and gold ZP positions. This paper uses no gold syntax. It builds a heterogeneous multi-task model on a shared Chinese BERT-base encoder, where each training example carries labels for only one task. Recovery is framed as per-word-space classification over pronoun types. Resolution is framed as extractive reading comprehension, with span (0,0), the [CLS] position, marking spaces that have no ZP. A lightly weighted binary ZP detection task is added as shared supervision. This offsets the size gap between the two datasets (OntoNotes 5.0 for resolution, BaiduZhidao for recovery). The joint model outperforms single-task BERT baselines and improves on earlier state-of-the-art results by about 3.4–3.5 F1 on both tasks. This shows that sharing scarce annotations across closely linked tasks pays off.

Key points

  • Data: BaiduZhidao (version cleaned by Yang et al., 2019) has 5504/1175/1178 train/dev/test instances. OntoNotes 5.0 has 36487 training and 6083 test instances, with 20% of training held out for development.
  • Main results (F1):
    • Resolution: 26.07, compared with 24.45 for BERT and 22.7 for ZPMN.
    • Recovery: 46.81, compared with 45.32 for BERT and 43.36 for NDPR-W.
    • Average: 36.44, compared with 34.89 for BERT.
  • Role of detection: Plain multi-task learning (BERT-MTL) helped resolution but reduced recovery F1 to 44.68. Adding the auxiliary detection loss (weight 0.1) resolved this conflict.
  • Gold settings:
    • Gold Tree + Gold ZP: 58.49 F1, compared with 57.3 for AttentionZP.
    • Gold Tree + Auto ZP: 36.55 F1, compared with 34.8 for ZPMN.
  • Automatic trees: Using automatic trees to constrain candidate spans raised resolution F1 from 26.07 to 34.12. The authors caution that this gain relies on accurate parsing of broadcast news. Code is available at github.com/freesunshine0316/lab-zp-joint.

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

Cited by

9 papers of our library cite it (of the 140673 whose bibliographies we read); 4 of them have a page here, 5 not yet.

Cite

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.18653/v1/2020.acl-main.482

@inproceedings{song2020zpr2,
  title = {{ZPR2: Joint Zero Pronoun Recovery and Resolution using Multi-Task Learning and BERT}},
  author = {Song, Linfeng and Xu, Kun and Zhang, Yue and Chen, Jianshu and Yu, Dong},
  booktitle = {Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics},
  year = {2020},
  doi = {10.18653/v1/2020.acl-main.482},
  url = {https://doi.org/10.18653/v1/2020.acl-main.482}
}

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