Cross-lingual and cross-domain discourse segmentation of entire documents

Chloé Braud, Ophélie Lacroix, Anders Søgaard


Abstract
Discourse segmentation is a crucial step in building end-to-end discourse parsers. However, discourse segmenters only exist for a few languages and domains. Typically they only detect intra-sentential segment boundaries, assuming gold standard sentence and token segmentation, and relying on high-quality syntactic parses and rich heuristics that are not generally available across languages and domains. In this paper, we propose statistical discourse segmenters for five languages and three domains that do not rely on gold pre-annotations. We also consider the problem of learning discourse segmenters when no labeled data is available for a language. Our fully supervised system obtains 89.5% F1 for English newswire, with slight drops in performance on other domains, and we report supervised and unsupervised (cross-lingual) results for five languages in total.
Anthology ID:
P17-2037
Volume:
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
Month:
July
Year:
2017
Address:
Vancouver, Canada
Editors:
Regina Barzilay, Min-Yen Kan
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
237–243
Language:
URL:
https://aclanthology.org/P17-2037
DOI:
10.18653/v1/P17-2037
Bibkey:
Cite (ACL):
Chloé Braud, Ophélie Lacroix, and Anders Søgaard. 2017. Cross-lingual and cross-domain discourse segmentation of entire documents. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 237–243, Vancouver, Canada. Association for Computational Linguistics.
Cite (Informal):
Cross-lingual and cross-domain discourse segmentation of entire documents (Braud et al., ACL 2017)
Copy Citation:
PDF:
https://aclanthology.org/P17-2037.pdf