Phylogenetic Multi-Lingual Dependency Parsing

Mathieu Dehouck 1, 2 Pascal Denis 2
2 MAGNET - Machine Learning in Information Networks
Inria Lille - Nord Europe, CRIStAL - Centre de Recherche en Informatique, Signal et Automatique de Lille (CRIStAL) - UMR 9189
Abstract : Languages evolve and diverge over time. Their evolutionary history is often depicted in the shape of a phylogenetic tree. Assuming parsing models are representations of their languages grammars, their evolution should follow a structure similar to that of the phylo-genetic tree. In this paper, drawing inspiration from multi-task learning, we make use of the phylogenetic tree to guide the learning of multilingual dependency parsers leverag-ing languages structural similarities. Experiments on data from the Universal Dependency project show that phylogenetic training is beneficial to low resourced languages and to well furnished languages families. As a side product of phylogenetic training, our model is able to perform zero-shot parsing of previously unseen languages.
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Mathieu Dehouck, Pascal Denis. Phylogenetic Multi-Lingual Dependency Parsing. NAACL 2019 - Annual Conference of the North American Chapter of the Association for Computational Linguistics, Jun 2019, Minneapolis, United States. ⟨hal-02143747⟩

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