Abstract
This paper explores the task of leveraging typology in the context of cross-lingual dependency parsing. While this linguistic information has shown great promise in pre-neural parsing, results for neural architectures have been mixed. The aim of our investigation is to better understand this state-of-the-art. Our main findings are as follows: 1) The benefit of typological information is derived from coarsely grouping languages into syntactically-homogeneous clusters rather than from learning to leverage variations along individual typological dimensions in a compositional manner; 2) Typology consistent with the actual corpus statistics yields better transfer performance; 3) Typological similarity is only a rough proxy of cross-lingual transferability with respect to parsing.- Anthology ID:
- D19-1574
- Volume:
- Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
- Month:
- November
- Year:
- 2019
- Address:
- Hong Kong, China
- Editors:
- Kentaro Inui, Jing Jiang, Vincent Ng, Xiaojun Wan
- Venues:
- EMNLP | IJCNLP
- SIG:
- SIGDAT
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 5714–5720
- Language:
- URL:
- https://aclanthology.org/D19-1574
- DOI:
- 10.18653/v1/D19-1574
- Cite (ACL):
- Adam Fisch, Jiang Guo, and Regina Barzilay. 2019. Working Hard or Hardly Working: Challenges of Integrating Typology into Neural Dependency Parsers. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 5714–5720, Hong Kong, China. Association for Computational Linguistics.
- Cite (Informal):
- Working Hard or Hardly Working: Challenges of Integrating Typology into Neural Dependency Parsers (Fisch et al., EMNLP-IJCNLP 2019)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/D19-1574.pdf
- Code
- ajfisch/TypologyParser