Abstract
Implicit discourse relation recognition is a challenging task as the relation prediction without explicit connectives in discourse parsing needs understanding of text spans and cannot be easily derived from surface features from the input sentence pairs. Thus, properly representing the text is very crucial to this task. In this paper, we propose a model augmented with different grained text representations, including character, subword, word, sentence, and sentence pair levels. The proposed deeper model is evaluated on the benchmark treebank and achieves state-of-the-art accuracy with greater than 48% in 11-way and F1 score greater than 50% in 4-way classifications for the first time according to our best knowledge.- Anthology ID:
- C18-1048
- Volume:
- Proceedings of the 27th International Conference on Computational Linguistics
- Month:
- August
- Year:
- 2018
- Address:
- Santa Fe, New Mexico, USA
- Venue:
- COLING
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 571–583
- Language:
- URL:
- https://aclanthology.org/C18-1048
- DOI:
- Cite (ACL):
- Hongxiao Bai and Hai Zhao. 2018. Deep Enhanced Representation for Implicit Discourse Relation Recognition. In Proceedings of the 27th International Conference on Computational Linguistics, pages 571–583, Santa Fe, New Mexico, USA. Association for Computational Linguistics.
- Cite (Informal):
- Deep Enhanced Representation for Implicit Discourse Relation Recognition (Bai & Zhao, COLING 2018)
- PDF:
- https://preview.aclanthology.org/auto-file-uploads/C18-1048.pdf
- Code
- diccooo/Deep_Enhanced_Repr_for_IDRR