arXiv:1602.01595cs.CL
Many Languages, One Parser
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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.
It provides basically a subset (maybe around a third) of the same interesting papers that I see in conference proceedings, but it provides them earlier (typically 1 month to 1 year earlier). This can be important as my field (NLP) is quite fast-paced. Just to give a concrete…
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