Abstract
Recently, sequence-to-sequence models have achieved impressive performance on a number of semantic parsing tasks. However, they often do not exploit available linguistic resources, while these, when employed correctly, are likely to increase performance even further. Research in neural machine translation has shown that employing this information has a lot of potential, especially when using a multi-encoder setup. We employ a range of semantic and syntactic resources to improve performance for the task of Discourse Representation Structure Parsing. We show that (i) linguistic features can be beneficial for neural semantic parsing and (ii) the best method of adding these features is by using multiple encoders.- Anthology ID:
- W19-0504
- Volume:
- Proceedings of the 13th International Conference on Computational Semantics - Short Papers
- Month:
- May
- Year:
- 2019
- Address:
- Gothenburg, Sweden
- Editors:
- Simon Dobnik, Stergios Chatzikyriakidis, Vera Demberg
- Venue:
- IWCS
- SIG:
- SIGSEM
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 24–31
- Language:
- URL:
- https://aclanthology.org/W19-0504
- DOI:
- 10.18653/v1/W19-0504
- Cite (ACL):
- Rik van Noord, Antonio Toral, and Johan Bos. 2019. Linguistic Information in Neural Semantic Parsing with Multiple Encoders. In Proceedings of the 13th International Conference on Computational Semantics - Short Papers, pages 24–31, Gothenburg, Sweden. Association for Computational Linguistics.
- Cite (Informal):
- Linguistic Information in Neural Semantic Parsing with Multiple Encoders (van Noord et al., IWCS 2019)
- PDF:
- https://preview.aclanthology.org/naacl24-info/W19-0504.pdf