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
- Editors:
- Satoshi Nakamura, Milica Gasic, Ingrid Zukerman, Gabriel Skantze, Mikio Nakano, Alexandros Papangelis, Stefan Ultes, Koichiro Yoshino
- 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
- 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)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-3/W19-5927.pdf