# Zero Resource Cross-Lingual Part Of Speech Tagging

arXiv: https://arxiv.org/abs/2401.05727 · HTML: https://mlsanity.com/paper/2401.05727/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 12 references that are in the map.
- First posted on arXiv 11 Jan 2024.

## Builds on (most cited first, 5 of 5)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding](https://mlsanity.com/paper/1804.07461/) (2018)
- [Unsupervised Cross-lingual Representation Learning at Scale](https://mlsanity.com/paper/1911.02116/) (2019)
- [SimAlign: High Quality Word Alignments without Parallel Training Data using Static and Contextualized Embeddings](https://mlsanity.com/paper/2004.08728/) (2020)
- [Model and Data Transfer for Cross-Lingual Sequence Labelling in Zero-Resource Settings](https://mlsanity.com/paper/2210.12623/) (2022)

## Built on by (most cited first, 2 of 2)

- [Unveiling Factors for Enhanced POS Tagging: A Study of Low-Resource Medieval Romance Languages](https://mlsanity.com/paper/2506.17715/) (2025)
- [Unsupervised Cross-Lingual Part-of-Speech Tagging with Monolingual Corpora Only](https://mlsanity.com/paper/2602.09366/) (2026)

## Abstract (arXiv)

Part of speech tagging in zero-resource settings can be an effective approach for low-resource languages when no labeled training data is available. Existing systems use two main techniques for POS tagging i.e. pretrained multilingual large language models(LLM) or project the source language labels into the zero resource target language and train a sequence labeling model on it. We explore the latter approach using the off-the-shelf alignment module and train a hidden Markov model(HMM) to predict the POS tags. We evaluate transfer learning setup with English as a source language and French, German, and Spanish as target languages for part-of-speech tagging. Our conclusion is that projected alignment data in zero-resource language can be beneficial to predict POS tags.