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        <title>Cross-lingual and cross-domain discourse segmentation of entire documents</title>
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    <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.</abstract>
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    <identifier type="doi">10.18653/v1/P17-2037</identifier>
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%0 Conference Proceedings
%T Cross-lingual and cross-domain discourse segmentation of entire documents
%A Braud, Chloé
%A Lacroix, Ophélie
%A Søgaard, Anders
%Y Barzilay, Regina
%Y Kan, Min-Yen
%S Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
%D 2017
%8 July
%I Association for Computational Linguistics
%C Vancouver, Canada
%F braud-etal-2017-cross-lingual
%X 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.
%R 10.18653/v1/P17-2037
%U https://aclanthology.org/P17-2037
%U https://doi.org/10.18653/v1/P17-2037
%P 237-243

Markdown (Informal)

[Cross-lingual and cross-domain discourse segmentation of entire documents](https://aclanthology.org/P17-2037) (Braud et al., ACL 2017)

• Cross-lingual and cross-domain discourse segmentation of entire documents (Braud et al., ACL 2017)

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.