@article{ammar-etal-2016-many,
title = "Many Languages, One Parser",
author = "Ammar, Waleed and
Mulcaire, George and
Ballesteros, Miguel and
Dyer, Chris and
Smith, Noah A.",
editor = "Lee, Lillian and
Johnson, Mark and
Toutanova, Kristina",
journal = "Transactions of the Association for Computational Linguistics",
volume = "4",
year = "2016",
address = "Cambridge, MA",
publisher = "MIT Press",
url = "https://aclanthology.org/Q16-1031",
doi = "10.1162/tacl_a_00109",
pages = "431--444",
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.",
}
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<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.</abstract>
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%0 Journal Article
%T Many Languages, One Parser
%A Ammar, Waleed
%A Mulcaire, George
%A Ballesteros, Miguel
%A Dyer, Chris
%A Smith, Noah A.
%J Transactions of the Association for Computational Linguistics
%D 2016
%V 4
%I MIT Press
%C Cambridge, MA
%F ammar-etal-2016-many
%X 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.
%R 10.1162/tacl_a_00109
%U https://aclanthology.org/Q16-1031
%U https://doi.org/10.1162/tacl_a_00109
%P 431-444
Markdown (Informal)
[Many Languages, One Parser](https://aclanthology.org/Q16-1031) (Ammar et al., TACL 2016)
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.