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 (finegrained 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. 2 While our parser can be used to parse input with codeswitching, we have not evaluated this capability due to the lack of appropriate data. 3 Although multilingual dependency treebanks have been available for a decade via the 2006 and 2007 CoNLL shared tasks (Buchholz and Marsi, 2006; Nivre et al., 2007) , the treebank of each language was annotated independently and with its own annotation conventions.
REF presented a multilingual model for parsing sentences, in which languages are encoded in their own variable.
Many Languages, One Parser
Transactions of the Association for Computational Linguistics
Transactions of the Association for Computational Linguistics
Computer Science