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Cross-Lingual Identification of Ambiguous Discourse Connectives for Resource-Poor Language

Summary

The paper tackles the identification of explicit discourse connectives in Chinese, a language that lacked a public discourse-annotated corpus. It addresses two kinds of ambiguity: whether a word is used as a connective at all, and which relation sense it signals. The authors propose a language-independent, cross-lingual pipeline built on English resources. First, English connectives from PDTB2 are translated with a bilingual dictionary and expanded with Chinese synonyms. Next, each Chinese candidate's sense ambiguity is scored as the entropy of a relation distribution. That distribution is projected from the English connectives aligned to the candidate in parallel data, and a threshold (max-e) filters out the noisiest candidates. Finally, a co-training scheme with an English view and a Chinese view separates discourse from non-discourse uses. It starts from PDTB2 labels and gradually absorbs unlabeled Chinese instances. For evaluation, the authors built DTBC, a new Chinese discourse corpus. The filtered lexicon and the co-training classifier clearly beat dictionary-only and machine-translation baselines. This shows that useful discourse tools can be bootstrapped for resource-poor languages.

Key points

  • DTBC: 500 news articles from Penn Chinese Tree Bank 6 (CTB6), annotated with 2,549 explicit relations, including connectives and arguments.
  • Lexicon quality: the entropy-filtered lexicon (DIC+ENT, 231 entries) reached F = 0.7773 (precision 0.8615). The raw dictionary list (561 entries) scored F = 0.6675. Over 30% of Chinese candidates turned out to be sense-ambiguous.
  • Explicit relation classification on DTBC: DIC+ENT achieved F = 0.7481 with max-e = 0.3, tuned on 20% of DTBC. The best baseline, DIC-1, reached F = 0.6827, and the MT-based approach reached F = 0.5825.
  • Discourse-usage identification: co-training with added lexical and word-alignment features (COT-2) obtained accuracy 0.7428 and F = 0.7933. The connective-only baseline scored 0.2470 / 0.3961, and MT-2 scored 0.6404 / 0.6814. Frequent words such as 和 and 在 rarely function as connectives, which makes this task harder in Chinese than in English.
  • Tools and resources: the pipeline uses PDTB2 top-level senses (Temporal, Contingency, Comparison, Expansion), Baidu Translator, Stanford Parser, MALLET, and the extended Chinese synonym list of Che et al. (2010).

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

Cited by

9 papers of our library cite it (of the 140673 whose bibliographies we read); 2 of them have a page here, 7 not yet.

Cite

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

@inproceedings{zhou2012cross,
  title = {{Cross-Lingual Identification of Ambiguous Discourse Connectives for Resource-Poor Language}},
  author = {Zhou, Lanjun and Gao, Wei and Li, Binyang and Wei, Zhongyu and Wong, Kam-Fai},
  booktitle = {Proceedings of COLING 2012: Posters},
  year = {2012},
  url = {https://aclanthology.org/C12-2138/}
}

References

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