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home / papers / 2022 / GCDT: A Chinese RST Treebank for Multigenre and Multilingual Discourse Parsing

GCDT: A Chinese RST Treebank for Multigenre and Multilingual Discourse Parsing

Summary

Chinese hierarchical discourse parsing has lacked large human-annotated RST data. Existing Chinese resources either build trees only within or between paragraphs, use dependency-style structures, or are small and use incompatible label sets. The authors built GCDT (Georgetown Chinese Discourse Treebank), an openly licensed (CC-BY) RST corpus of 50 Mandarin documents. It follows the design of the English GUM corpus: five shared genres, the same two-level relation inventory, and EDU segmentation based on syntax rather than punctuation. The segmentation also covers prenominal relative clauses. Annotation quality is comparable to the English RST-DT benchmark.

Using the pointer-network parser of Liu et al. (2021), the authors set monolingual baselines for GCDT and report the first published scores on GUM V8.0.0. They also trained jointly on Chinese and English, with finetuning and automatic EDU translation. Cross-lingual training improved results in both languages, while finetuning helped only on the smaller Chinese data. The resource fills a gap for non-European RST parsing and supports multilingual, multi-genre research.

Key points

  • Corpus size and sources: GCDT has 50 documents (10 each of academic, bio, interview, news, whow), 62,905 tokens and 9,710 EDUs, averaging about 6.5 tokens per EDU. Texts come from hanspub, Chinese Wikipedia, Wikinews and wikiHow. The split is 8-1-1 per genre.
  • Annotation scheme: Relations use GUM V8.0.0 labels (15 coarse, 32 fine-grained) and were annotated in rstWeb. GCDT is reportedly the first RST corpus to segment prenominal relative clauses, using same-unit for the resulting discontinuous EDUs.
  • Inter-annotator agreement: Segmentation agreement is 97.4% token-wise (Cohen's κ = 0.89). Span/Nuclearity/Relation agreement is 84.27/66.15/57.77 (original Parseval), close to RST-DT's 78.7/66.8/57.1.
  • Parsing results: The best monolingual GCDT model (hfl/chinese-roberta-wwm-ext) scores 75.51/57.08/51.76. Adding GUM-12 with en→zh translation and Chinese RoBERTa raises Span to 78.11, and adding GUM-12 with finetuning reaches Rel 55.28.
  • Embeddings and genres: Monolingual RoBERTa embeddings generally beat multilingual ones (bert-base-multilingual-cased, xlm-roberta-base). Finetuning lowered GUM scores. Performance varies by genre: whow is easiest and academic hardest, for both models and humans.

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: Discourse analysis · Treebanks

Cited by

19 papers of our library cite it (of the 140673 whose bibliographies we read); 8 of them have a page here, 11 not yet.

Cite

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.18653/v1/2022.aacl-short.47 · arXiv 2210.10449

@inproceedings{peng2022gcdt,
  title = {{GCDT: A Chinese RST Treebank for Multigenre and Multilingual Discourse Parsing}},
  author = {Peng, Siyao and Liu, Yang Janet and Zeldes, Amir},
  booktitle = {Proceedings of the 2nd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 12th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)},
  year = {2022},
  doi = {10.18653/v1/2022.aacl-short.47},
  eprint = {2210.10449},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.18653/v1/2022.aacl-short.47}
}

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