Abstract
Not all dependencies are equal when training a dependency parser: some are straightforward enough to be learned with only a sample of data, others embed more complexity. This work introduces a series of metrics to quantify those differences, and thereby to expose the shortcomings of various parsing algorithms and strategies. Apart from a more thorough comparison of parsing systems, these new tools also prove useful for characterizing the information conveyed by cross-lingual parsers, in a quantitative but still interpretable way.- Anthology ID:
- C18-1270
- 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:
- 3191–3202
- Language:
- URL:
- https://aclanthology.org/C18-1270
- DOI:
- Cite (ACL):
- Lauriane Aufrant, Guillaume Wisniewski, and François Yvon. 2018. Quantifying training challenges of dependency parsers. In Proceedings of the 27th International Conference on Computational Linguistics, pages 3191–3202, Santa Fe, New Mexico, USA. Association for Computational Linguistics.
- Cite (Informal):
- Quantifying training challenges of dependency parsers (Aufrant et al., COLING 2018)
- PDF:
- https://preview.aclanthology.org/emnlp-22-attachments/C18-1270.pdf