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Out-of-Domain Discourse Dependency Parsing via Bootstrapping: An Empirical Analysis on Its Effectiveness and Limitation

Paper · 2022 · Noriki Nishida and Yuji Matsumoto

Summary

Discourse dependency parsers trained on one annotated treebank lose much of their accuracy on text from a different domain, and labeled data for new domains is scarce and expensive. Nishida and Matsumoto test whether bootstrapping can narrow this gap without any target-domain annotation. They adapt BERT-based graph-based and transition-based discourse dependency parsers using self-training, co-training, tri-training and asymmetric tri-training. They also systematically compare confidence measures and rules for selecting pseudo-labeled documents. Two settings are examined: monologue transfer between scientific disciplines and transfer across dialogue genres. The authors also release COVID19-DTB, a new manually annotated treebank of biomedical paper abstracts. Bootstrapping gives significant and consistent improvements in every setting. However, reliable pseudo labels concentrate in simpler documents, so complex texts stay hard to parse, and feeding in more unlabeled data does not solve this. Annotating a few carefully chosen documents through active learning helps where bootstrapping stalls. The work offers practical guidance for applying discourse parsing in domains that lack a treebank.

Key points

  • New resource: COVID19-DTB, discourse dependency annotations for COVID-19-related biomedical abstracts. The data is available in the biomedical-discourse-treebanks GitHub repository, and the code is in norikinishida/discourse-parsing.
  • Other corpora mentioned include RST-DT (only 385 labeled documents, news), SciDTB (NLP abstracts), and STAC and Molweni (multi-party dialogues).
  • The model's own predictive probability is a usable filter for noisy pseudo labels because it correlates with label accuracy. The authors recommend the Rank-above-k selection criterion over Rank-diff-k, since its k does not depend on the size of the unlabeled pool.
  • Active learning simulation: the 100 least-confident dialogues from the Molweni training set (about 9K dialogues) were manually labeled and used for fine-tuning. This beat randomly chosen samples (averaged over 5 trials), and combining it with co-training outperformed active learning on the source-only model.
  • Limitation: all experiments use English only, and extending the study to other languages would require discourse treebanks in those languages.

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

 Institutions

🔬RIKEN Center for Advanced Intelligence ProjectTokyo · Japan15 works · 957 citations

 Fields, topics and concepts

📚 Shelves 4The field of science this work belongs to (OpenAlex fields).Computer Science · 16,667 worksArtificial Intelligence · 16,194 worksArts and Humanities · 16,039 worksLanguage and Linguistics · 15,891 works

🧭 Topics 2Research subjects and languages the work is about.Discourse analysis · 466 worksDependency parsing · 131 works

Cite

Cite as Noriki Nishida and Yuji Matsumoto. 2022. Out-of-Domain Discourse Dependency Parsing via Bootstrapping: An Empirical Analysis on Its Effectiveness and Limitation. Transactions of the Association for Computational Linguistics.

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.1162/tacl_a_00451

@article{nishida2022out,
  title = {{Out-of-Domain Discourse Dependency Parsing via Bootstrapping: An Empirical Analysis on Its Effectiveness and Limitation}},
  author = {Noriki Nishida and Yuji Matsumoto},
  journal = {Transactions of the Association for Computational Linguistics},
  year = {2022},
  doi = {10.1162/tacl\_a\_00451},
  url = {https://doi.org/10.1162/tacl_a_00451}
}

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