Papers › Many Languages, One Parser
Many Languages, One Parser
Waleed Ammar, George Mulcaire, Miguel Ballesteros, Chris Dyer, Noah A. Smith
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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Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Cross-lingual zero-shot dependency parsing | Universal Dependency Treebank | MaLOPa | LAS | 70.5 | #2 of 3 | Archive leaderboard | report |
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