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Zero-shot transfer for implicit discourse relation classification
Stockholm University, Faculty of Humanities, Department of Linguistics, Computational Linguistics.
Stockholm University, Faculty of Humanities, Department of Linguistics, Computational Linguistics.
2019 (English)In: 20th Annual Meeting of the Special Interest Group on Discourse and Dialogue: Proceedings of the Conference, Association for Computational Linguistics, 2019, p. 226-231Conference paper, Published paper (Refereed)
Abstract [en]

Automatically classifying the relation between sentences in a discourse is a challenging task, in particular when there is no overt expression of the relation. It becomes even more challenging by the fact that annotated training data exists only for a small number of languages, such as English and Chinese. We present a new system using zero-shot transfer learning for implicit discourse relation classification, where the only resource used for the target language is unannotated parallel text. This system is evaluated on the discourse-annotated TEDMDB parallel corpus, where it obtains good results for all seven languages using only English training data.

Place, publisher, year, edition, pages
Association for Computational Linguistics, 2019. p. 226-231
National Category
Language Technology (Computational Linguistics)
Research subject
Computational Linguistics
Identifiers
URN: urn:nbn:se:su:diva-173473OAI: oai:DiVA.org:su-173473DiVA, id: diva2:1354077
Conference
SIGdial 2019, Stockholm, Sweden, September 11-13, 2019
Available from: 2019-09-24 Created: 2019-09-24 Last updated: 2019-09-25Bibliographically approved

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Kurfali, MurathanÖstling, Robert
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