Cross-lingual and cross-domain discourse segmentation of entire documents
By Chloé Braud, Ophélie Lacroix, Anders Søgaard. 2017
36 citations recorded by the source corpus.
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.
Subfields: Artificial Intelligence
How researchers see this paper
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In Artificial Intelligence
- Sebastian Ruder
- Ivan Vulic
- Anders Søgaard
- Iryna Gurevych
- Jonas Pfeiffer
- Goran Glavas
- Graham Neubig
- Noah A. Smith
Citations in the corpus
- A Survey of Cross-lingual Word Embedding Models (2017, background): “Braud, Lacroix, and Søgaard (2017) and Braud, Coavoux, and Søgaard (2017) perform experiments using a diverse range of RST discourse treebanks for English, Portuguese, Spanish, German, Dutch, and Basque.”