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

MCML Authors

Ayyoob Imani

→ Group Hinrich Schütze
 Computational Linguistics

Masoud Jalili Sabet

Dr.

* Former Member

→ Group Hinrich Schütze
 Computational Linguistics

Hinrich Schütze

Prof. Dr.

Principal Investigator

Computational Linguistics

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.

inproceedings

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EMNLP 2022

Conference on Empirical Methods in Natural Language Processing. Abu Dhabi, United Arab Emirates, Nov 07-11, 2022.

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Authors

A. Imani • S. Severini • M. J. Sabet • F. Yvon • H. Schütze

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Links

DOI

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Research Area

 B2 | Natural Language Processing