Abstract
We explore whether it is possible to build lighter parsers, that are statistically equivalent to their corresponding standard version, for a wide set of languages showing different structures and morphologies. As testbed, we use the Universal Dependencies and transition-based dependency parsers trained on feed-forward networks. For these, most existing research assumes de facto standard embedded features and relies on pre-computation tricks to obtain speed-ups. We explore how these features and their size can be reduced and whether this translates into speed-ups with a negligible impact on accuracy. The experiments show that grand-daughter features can be removed for the majority of treebanks without a significant (negative or positive) LAS difference. They also show how the size of the embeddings can be notably reduced.- Anthology ID:
- W18-6019
- Volume:
- Proceedings of the Second Workshop on Universal Dependencies (UDW 2018)
- Month:
- November
- Year:
- 2018
- Address:
- Brussels, Belgium
- Venue:
- UDW
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 162–172
- Language:
- URL:
- https://aclanthology.org/W18-6019
- DOI:
- 10.18653/v1/W18-6019
- Cite (ACL):
- David Vilares and Carlos Gómez-Rodríguez. 2018. Transition-based Parsing with Lighter Feed-Forward Networks. In Proceedings of the Second Workshop on Universal Dependencies (UDW 2018), pages 162–172, Brussels, Belgium. Association for Computational Linguistics.
- Cite (Informal):
- Transition-based Parsing with Lighter Feed-Forward Networks (Vilares & Gómez-Rodríguez, UDW 2018)
- PDF:
- https://preview.aclanthology.org/remove-xml-comments/W18-6019.pdf