Cross-Lingual Morphological Tagging for Low-Resource Languages
Venue
Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), Association for Computational Linguistics, Berlin, Germany (2016), pp. 1954-1964
Publication Year
2016
Authors
Jan Buys, Jan A. Botha
BibTeX
Abstract
Morphologically rich languages often lack the annotated linguistic resources
required to develop accurate natural language processing tools. We propose models
suitable for training morphological taggers with rich tagsets for low-resource
languages without using direct supervision. Our approach extends existing
approaches of projecting part-of-speech tags across languages, using bitext to
infer constraints on the possible tags for a given word type or token. We propose a
tagging model using Wsabie, a discriminative embedding-based model with rank-based
learning. In our evaluation on 11 languages, on average this model performs on par
with a baseline weakly-supervised HMM, while being more scalable. Multilingual
experiments show that the method performs best when projecting between related
language pairs. Despite the inherently lossy projection, we show that the
morphological tags predicted by our models improve the downstream performance of a
parser by +0.6 LAS on average.
