Zero-shot transfer for implicit discourse relation classification

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Authors Murathan Kurfalı, Robert Östling
Journal/Conference Name SIGDIAL 2019 - 20th Annual Meeting of the Special Interest Group Discourse Dialogue - Proceedings of the Conference
Paper Category
Paper Abstract 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 TED-MDB parallel corpus, where it obtains good results for all seven languages using only English training data.
Date of publication 2019
Code Programming Language Python
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