Many Languages, One Parser

Abstract

We train a language-universal dependency parser on a multilingual collection of treebanks. The parsing model uses multilingual word embeddings alongside learned and specified typological information, enabling generalization based on linguistic universals and based on typological similarities. We evaluate our parser’s performance on languages in the training… (More)

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Cite this paper

@article{Ammar2016ManyLO, title={Many Languages, One Parser}, author={Waleed Ammar and George Mulcaire and Miguel Ballesteros and Chris Dyer and Noah A. Smith}, journal={TACL}, year={2016}, volume={4}, pages={431-444} }