@inproceedings{imanigooghari-etal-2022-graph,
title = "Graph-Based Multilingual Label Propagation for Low-Resource Part-of-Speech Tagging",
author = {ImaniGooghari, Ayyoob and
Severini, Silvia and
Jalili Sabet, Masoud and
Yvon, Fran{\c{c}}ois and
Sch{\"u}tze, Hinrich},
editor = "Goldberg, Yoav and
Kozareva, Zornitsa and
Zhang, Yue",
booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing",
month = dec,
year = "2022",
address = "Abu Dhabi, United Arab Emirates",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.emnlp-main.102",
doi = "10.18653/v1/2022.emnlp-main.102",
pages = "1577--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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T Graph-Based Multilingual Label Propagation for Low-Resource Part-of-Speech Tagging
%A ImaniGooghari, Ayyoob
%A Severini, Silvia
%A Jalili Sabet, Masoud
%A Yvon, François
%A Schütze, Hinrich
%Y Goldberg, Yoav
%Y Kozareva, Zornitsa
%Y Zhang, Yue
%S Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
%D 2022
%8 December
%I Association for Computational Linguistics
%C Abu Dhabi, United Arab Emirates
%F imanigooghari-etal-2022-graph
%X 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.
%R 10.18653/v1/2022.emnlp-main.102
%U https://aclanthology.org/2022.emnlp-main.102
%U https://doi.org/10.18653/v1/2022.emnlp-main.102
%P 1577-1589
Markdown (Informal)
[Graph-Based Multilingual Label Propagation for Low-Resource Part-of-Speech Tagging](https://aclanthology.org/2022.emnlp-main.102) (ImaniGooghari et al., EMNLP 2022)
ACL