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

Imani, Ayyoob; Severini, Silvia; Sabet, Masoud Jalili; Yvon, François und Schütze, Hinrich (Dezember 2022): Graph-Based Multilingual Label Propagation for Low-Resource Part-of-Speech Tagging. EMNLP 2022, Abu Dhabi, United Arab Emirates, December 7-11, 2022. In: Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, S. 157-1589

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.

• Dokumententyp: Konferenzbeitrag (Paper)
• EU Funded Grant Agreement Number: 740516
• EU-Projekte: Horizon 2020 > ERC Grants > ERC Advanced Grant > ERC Grant 740516: NonSequeToR - Non-sequence models for tokenization replacement
• Publikationsform: Publisher's Version
• Fakultätsübergreifende Einrichtungen: Centrum für Informations- und Sprachverarbeitung (CIS)
• Themengebiete: 400 Sprache > 400 Sprache ; 400 Sprache > 410 Linguistik
• Sprache: Englisch