TL;DR
This paper presents a unified multilingual dependency parser that leverages language-specific features and universal linguistic properties, enabling effective parsing across multiple languages with limited annotated data.
Contribution
The authors introduce a single multilingual parser that uses language-specific features and universal representations, improving cross-lingual parsing performance especially with limited data.
Findings
Performs well across languages with varying data sizes
Generalizes effectively based on linguistic universals
Outperforms strong baselines in multiple scenarios
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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