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

arXiv: https://arxiv.org/abs/2210.09840 · HTML: https://mlsanity.com/paper/2210.09840/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 28 of its 68 references that are in the map.
- First posted on arXiv 18 Oct 2022.

## Builds on (most cited first, 28 of 28)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Unsupervised Cross-lingual Representation Learning at Scale](https://mlsanity.com/paper/1911.02116/) (2019)
- [The State and Fate of Linguistic Diversity and Inclusion in the NLP World](https://mlsanity.com/paper/2004.09095/) (2020)
- [Convolutional Networks on Graphs for Learning Molecular Fingerprints](https://mlsanity.com/paper/1509.09292/) (2015)
- [Stanza: A Python Natural Language Processing Toolkit for Many Human Languages](https://mlsanity.com/paper/2003.07082/) (2020)
- [Choosing Transfer Languages for Cross-Lingual Learning](https://mlsanity.com/paper/1905.12688/) (2019)
- [Encoding Sentences with Graph Convolutional Networks for Semantic Role Labeling](https://mlsanity.com/paper/1703.04826/) (2017)
- [Dynamic Multiscale Graph Neural Networks for 3D Skeleton-Based Human Motion Prediction](https://mlsanity.com/paper/2003.08802/) (2020)
- [75 Languages, 1 Model: Parsing Universal Dependencies Universally](https://mlsanity.com/paper/1904.02099/) (2019)
- [Graph Convolutional Encoders for Syntax-aware Neural Machine Translation](https://mlsanity.com/paper/1704.04675/) (2017)
- [Graph-to-Sequence Learning using Gated Graph Neural Networks](https://mlsanity.com/paper/1806.09835/) (2018)
- [How to Adapt Your Pretrained Multilingual Model to 1600 Languages](https://mlsanity.com/paper/2106.02124/) (2021)
- [Explicit Alignment Objectives for Multilingual Bidirectional Encoders](https://mlsanity.com/paper/2010.07972/) (2020)
- [Revisiting the Primacy of English in Zero-shot Cross-lingual Transfer](https://mlsanity.com/paper/2106.16171/) (2021)
- [Expanding Pretrained Models to Thousands More Languages via Lexicon-based Adaptation](https://mlsanity.com/paper/2203.09435/) (2022)
- [Small and Practical BERT Models for Sequence Labeling](https://mlsanity.com/paper/1909.00100/) (2019)
- [Sentence-State LSTM for Text Representation](https://mlsanity.com/paper/1805.02474/) (2018)
- [On Node Features for Graph Neural Networks](https://mlsanity.com/paper/1911.08795/) (2019)
- [Cross-lingual, Character-Level Neural Morphological Tagging](https://mlsanity.com/paper/1708.09157/) (2017)
- [Axioms for Centrality](https://mlsanity.com/paper/1308.2140/) (2013)
- [Cross-Lingual Morphological Tagging for Low-Resource Languages](https://mlsanity.com/paper/1606.04279/) (2016)
- [Learning when to trust distant supervision: An application to low-resource POS tagging using cross-lingual projection](https://mlsanity.com/paper/1607.01133/) (2016)
- [Distant Supervision from Disparate Sources for Low-Resource Part-of-Speech Tagging](https://mlsanity.com/paper/1808.09733/) (2018)
- [Towards a Broad Coverage Named Entity Resource: A Data-Efficient Approach for Many Diverse Languages](https://mlsanity.com/paper/2201.12219/) (2022)
- [A Strong Baseline for Learning Cross-Lingual Word Embeddings from Sentence Alignments](https://mlsanity.com/paper/1608.05426/) (2016)
- [Graph Neural Networks for Multiparallel Word Alignment](https://mlsanity.com/paper/2203.08654/) (2022)
- [ParCourE: A Parallel Corpus Explorer for a Massively Multilingual Corpus](https://mlsanity.com/paper/2107.06632/) (2021)

## Built on by (most cited first, 4 of 4)

- [Glot500: Scaling Multilingual Corpora and Language Models to 500 Languages](https://mlsanity.com/paper/2305.12182/) (2023)
- [The taggedPBC: Annotating a massive parallel corpus for crosslinguistic investigations](https://mlsanity.com/paper/2505.12560/) (2025)
- [Unveiling Factors for Enhanced POS Tagging: A Study of Low-Resource Medieval Romance Languages](https://mlsanity.com/paper/2506.17715/) (2025)
- [Extending dependencies to the taggedPBC: Word order in transitive clauses](https://mlsanity.com/paper/2506.06785/) (2025)

## Abstract (arXiv)

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.