Deep Neural Networks for Syntactic Parsing of Morphologically Rich Languages

Abstract

Morphologically rich languages (MRL) are languages in which much of the structural information is contained at the wordlevel, leading to high level word-form variation. Historically, syntactic parsing has been mainly tackled using generative models. These models assume input features to be conditionally independent, making difficult to incorporate arbitrary features. In this paper, we investigate the greedy discriminative parser described in (Legrand and Collobert, 2015), which relies on word embeddings, in the context of MRL. We propose to learn morphological embeddings and propagate morphological information through the tree using a recursive composition procedure. Experiments show that such embeddings can dramatically improve the average performance on different languages. Moreover, it yields state-of-the art performance for a majority of languages.