Many Languages, One Parser
Waleed Ammar, George Mulcaire, Miguel Ballesteros, Chris Dyer, Noah A. Smith
Abstract
We train one multilingual model for dependency parsing and use it to parse sentences in several languages. The parsing model uses (i) multilingual word clusters and embeddings; (ii) token-level language information; and (iii) language-specific features (fine-grained POS tags). This input representation enables the parser not only to parse effectively in multiple languages, but also to generalize across languages based on linguistic universals and typological similarities, making it more effective to learn from limited annotations. Our parser’s performance compares favorably to strong baselines in a range of data scenarios, including when the target language has a large treebank, a small treebank, or no treebank for training.- Anthology ID:
- Q16-1031
- Volume:
- Transactions of the Association for Computational Linguistics, Volume 4
- Month:
- Year:
- 2016
- Address:
- Cambridge, MA
- Editors:
- Lillian Lee, Mark Johnson, Kristina Toutanova
- Venue:
- TACL
- SIG:
- Publisher:
- MIT Press
- Note:
- Pages:
- 431–444
- Language:
- URL:
- https://aclanthology.org/Q16-1031
- DOI:
- 10.1162/tacl_a_00109
- Cite (ACL):
- Waleed Ammar, George Mulcaire, Miguel Ballesteros, Chris Dyer, and Noah A. Smith. 2016. Many Languages, One Parser. Transactions of the Association for Computational Linguistics, 4:431–444.
- Cite (Informal):
- Many Languages, One Parser (Ammar et al., TACL 2016)
- PDF:
- https://preview.aclanthology.org/ingest-bitext-workshop/Q16-1031.pdf
- Code
- clab/language-universal-parser
- Data
- Universal Dependencies