Zero-shot transfer for implicit discourse relation classification

Murathan Kurfalı, Robert Östling


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.
Anthology ID:
W19-5927
Volume:
Proceedings of the 20th Annual SIGdial Meeting on Discourse and Dialogue
Month:
September
Year:
2019
Address:
Stockholm, Sweden
Venue:
SIGDIAL
SIG:
SIGDIAL
Publisher:
Association for Computational Linguistics
Note:
Pages:
226–231
Language:
URL:
https://aclanthology.org/W19-5927
DOI:
10.18653/v1/W19-5927
Bibkey:
Cite (ACL):
Murathan Kurfalı and Robert Östling. 2019. Zero-shot transfer for implicit discourse relation classification. In Proceedings of the 20th Annual SIGdial Meeting on Discourse and Dialogue, pages 226–231, Stockholm, Sweden. Association for Computational Linguistics.
Cite (Informal):
Zero-shot transfer for implicit discourse relation classification (Kurfalı & Östling, SIGDIAL 2019)
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PDF:
https://aclanthology.org/W19-5927.pdf