Graph-Based Multilingual Label Propagation for Low-Resource Part-of-Speech Tagging

TL;DR

This paper introduces a graph neural network-based label propagation method for transferring POS tags from high-resource to low-resource languages, achieving state-of-the-art results in unsupervised POS tagging.

Contribution

It presents a novel graph-based label propagation approach using GNNs and transformers for multilingual POS tagging in low-resource languages.

Findings

Achieves new state-of-the-art in unsupervised low-resource POS tagging.

Effective label transfer from multiple high-resource languages.

Improves POS tagging accuracy with enhanced contextual embeddings.

Abstract

Part-of-Speech (POS) tagging is an important component of the NLP pipeline, but many low-resource 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-of-the-art for unsupervised POS tagging of low-resource languages.