Evaluating Globally Normalized Transition Based Neural Networks for Multilingual Natural Language Understanding

We analyze globally normalized transition-based neural network models for dependency parsing on English, German, Spanish, and Catalan. We compare the results with FreeLing, an open source language analysis tool developed at the UPC natural language processing research group. Furthermore we study how the mini-batch size, the number of units in the hidden layers and the beam width affect the performances of the network. Finally we propose a multi-lingual parser with parameters sharing and experiment with German and English obtaining a significant accuracy improvement upon the monolingual parsers. These multi-lingual parsers can be used for low-resource languages of for all the applications with low memory requirements, where having one model per language in intractable.