Fine-Grained Analysis of Cross-Linguistic Syntactic Divergences
Dmitry Nikolaev, Ofir Arviv, Taelin Karidi, Neta Kenneth, Veronika Mitnik, Lilja Maria Saeboe, Omri Abend
Abstract
The patterns in which the syntax of different languages converges and diverges are often used to inform work on cross-lingual transfer. Nevertheless, little empirical work has been done on quantifying the prevalence of different syntactic divergences across language pairs. We propose a framework for extracting divergence patterns for any language pair from a parallel corpus, building on Universal Dependencies. We show that our framework provides a detailed picture of cross-language divergences, generalizes previous approaches, and lends itself to full automation. We further present a novel dataset, a manually word-aligned subset of the Parallel UD corpus in five languages, and use it to perform a detailed corpus study. We demonstrate the usefulness of the resulting analysis by showing that it can help account for performance patterns of a cross-lingual parser.- Anthology ID:
- 2020.acl-main.109
- Volume:
- Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
- Month:
- July
- Year:
- 2020
- Address:
- Online
- Editors:
- Dan Jurafsky, Joyce Chai, Natalie Schluter, Joel Tetreault
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1159–1176
- Language:
- URL:
- https://aclanthology.org/2020.acl-main.109
- DOI:
- 10.18653/v1/2020.acl-main.109
- Cite (ACL):
- Dmitry Nikolaev, Ofir Arviv, Taelin Karidi, Neta Kenneth, Veronika Mitnik, Lilja Maria Saeboe, and Omri Abend. 2020. Fine-Grained Analysis of Cross-Linguistic Syntactic Divergences. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 1159–1176, Online. Association for Computational Linguistics.
- Cite (Informal):
- Fine-Grained Analysis of Cross-Linguistic Syntactic Divergences (Nikolaev et al., ACL 2020)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/2020.acl-main.109.pdf
- Code
- macleginn/exploring-clmd-divergences
- Data
- Universal Dependencies