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Pseudo Zero Pronoun Resolution Improves Zero Anaphora Resolution

Summary

Zero anaphora resolution (ZAR) means finding the antecedent of an omitted argument (a "zero pronoun") in pro-drop languages like Japanese. The authors aim to get more from pretrained masked language models (MLMs) on this task with two ideas. First, they add a pretraining task called pseudo zero pronoun resolution (PZERO), built cheaply from raw text. When a noun phrase recurs in a passage, one occurrence is collapsed into a single [MASK] token. The model must then point to its earlier same-form occurrences, which gives it explicit (if noisy) supervision on anaphoric links. Second, they design a finetuning model, AS-PZERO, that keeps all pretrained parameters. It phrases each query as a masked prompt, which narrows the gap between pretraining and finetuning. On the NAIST Text Corpus (NTC), both ideas improve results and set new state-of-the-art F1 scores. The analysis shows that arguments far from the predicate and arguments of passive predicates remain hard. This makes the work a useful recipe for low-resource anaphora tasks.

Key points

  • PZERO data generation: All NPs sharing a surface form are treated as coreferent, which allows large-scale pseudo data to be mined from raw corpora. The whole masked NP becomes one [MASK] token, so the model cannot use mask count as a clue. The target is the NP's last token, matching NTC's annotation convention. Training minimizes a KL-divergence loss over candidate token positions.
  • Finetuning models: The baseline is an argument-selection model (AS). The proposed AS-PZERO appends a query chunk ([MASK] + case marker + predicate), giving a prompt-like setup with minimal architectural change.
  • Benchmark: NTC with NOM, ACC and DAT roles. Evaluation covers intra-sentential, inter-sentential and exophoric (author/reader/general) arguments in a single formulation.
  • Development-set recall analysis: Between the two compared configurations, recall rises from 68.9 to 71.09 for intra-sentential and from 43.11 to 45.65 for inter-sentential arguments. Passive predicates lag behind active ones (about 52–58 vs 71–73 intra). Arguments outside the input window get 0.0 recall.
  • Code: Available at github.com/Ryuto10/pzero-improves-zar. Analyzing noise in the same-surface-form assumption is left as future work.

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

Cite

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.18653/v1/2021.emnlp-main.308 · arXiv 2104.07425

@inproceedings{konno2021pseudo,
  title = {{Pseudo Zero Pronoun Resolution Improves Zero Anaphora Resolution}},
  author = {Konno, Ryuto and Kiyono, Shun and Matsubayashi, Yuichiroh and Ouchi, Hiroki and Inui, Kentaro},
  booktitle = {Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing},
  year = {2021},
  doi = {10.18653/v1/2021.emnlp-main.308},
  eprint = {2104.07425},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.18653/v1/2021.emnlp-main.308}
}

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