Graph-Based Multilingual Label Propagation for Low-Resource Part-of-Speech Tagging
Ayyoob ImaniGooghari, Silvia Severini, Masoud Jalili Sabet, François Yvon, Hinrich Schütze · 2022
Part-of-Speech (POS) tagging is an important component of the NLP pipeline, but many lowresource languages lack labeled data for training.An established method for training a POS tagger in such a scenario is to create a labeled training set by transferring from high-resource languages.In this paper, we propose a novel method for transferring labels from multiple high-resource source to low-resource target languages.We formalize POS tag projection as graph-based label propagation.Given translations of a sentence in multiple languages, we create a graph with words as nodes and alignment links as edges by aligning words for all language pairs.We then propagate node labels from source to target using a Graph Neural Network augmented with transformer layers.We show that our propagation creates training sets that allow us to train POS taggers for a diverse set of languages.When combined with enhanced contextualized embeddings, our method achieves a new state-ofthe-art for unsupervised POS tagging of low resource languages.