Abstract
This paper describes our participation in the shared task of Discourse Representation Structure parsing. It follows the work of Van Noord et al. (2018), who employed a neural sequence-to-sequence model to produce DRSs, also exploiting linguistic information with multiple encoders. We provide a detailed look in the performance of this model and show that (i) the benefit of the linguistic features is evident across a number of experiments which vary the amount of training data and (ii) the model can be improved by applying a number of postprocessing methods to fix ill-formed output. Our model ended up in second place in the competition, with an F-score of 84.5.- Anthology ID:
- W19-1204
- Volume:
- Proceedings of the IWCS Shared Task on Semantic Parsing
- Month:
- May
- Year:
- 2019
- Address:
- Gothenburg, Sweden
- Editors:
- Lasha Abzianidze, Rik van Noord, Hessel Haagsma, Johan Bos
- Venue:
- IWCS
- SIG:
- SIGSEM
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- Language:
- URL:
- https://aclanthology.org/W19-1204
- DOI:
- 10.18653/v1/W19-1204
- Cite (ACL):
- Rik van Noord. 2019. Neural Boxer at the IWCS Shared Task on DRS Parsing. In Proceedings of the IWCS Shared Task on Semantic Parsing, Gothenburg, Sweden. Association for Computational Linguistics.
- Cite (Informal):
- Neural Boxer at the IWCS Shared Task on DRS Parsing (van Noord, IWCS 2019)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/W19-1204.pdf