Abstract
Identifying discourse relations that are not overtly marked with discourse connectives remains a challenging problem. The absence of explicit clues indicates a need for the combination of world knowledge and weak contextual clues, which can hardly be learned from a small amount of manually annotated data. In this paper, we address this problem by augmenting the input text with external knowledge and context and by adopting a neural network model that can effectively handle the augmented text. Experiments show that external knowledge did improve the classification accuracy. Contextual information provided no significant gain for implicit discourse relations, but it did for explicit ones.- Anthology ID:
- C18-1049
- Volume:
- Proceedings of the 27th International Conference on Computational Linguistics
- Month:
- August
- Year:
- 2018
- Address:
- Santa Fe, New Mexico, USA
- Editors:
- Emily M. Bender, Leon Derczynski, Pierre Isabelle
- Venue:
- COLING
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 584–595
- Language:
- URL:
- https://aclanthology.org/C18-1049
- DOI:
- Cite (ACL):
- Yudai Kishimoto, Yugo Murawaki, and Sadao Kurohashi. 2018. A Knowledge-Augmented Neural Network Model for Implicit Discourse Relation Classification. In Proceedings of the 27th International Conference on Computational Linguistics, pages 584–595, Santa Fe, New Mexico, USA. Association for Computational Linguistics.
- Cite (Informal):
- A Knowledge-Augmented Neural Network Model for Implicit Discourse Relation Classification (Kishimoto et al., COLING 2018)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-3/C18-1049.pdf