# Multilingual Part-of-Speech Tagging: Two Unsupervised Approaches

arXiv: https://arxiv.org/abs/1401.5695 · HTML: https://mlsanity.com/paper/1401.5695/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Jan 2014.

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

- [Multitask Learning for Class-Imbalanced Discourse Classification](https://mlsanity.com/paper/2101.00389/) (2021)
- [Indirect Question Answering in English, German and Bavarian: A Challenging Task for High- and Low-Resource Languages Alike](https://mlsanity.com/paper/2603.15130/) (2026)

## Abstract (arXiv)

We demonstrate the effectiveness of multilingual learning for unsupervised part-of-speech tagging. The central assumption of our work is that by combining cues from multiple languages, the structure of each becomes more apparent. We consider two ways of applying this intuition to the problem of unsupervised part-of-speech tagging: a model that directly merges tag structures for a pair of languages into a single sequence and a second model which instead incorporates multilingual context using latent variables. Both approaches are formulated as hierarchical Bayesian models, using Markov Chain Monte Carlo sampling techniques for inference. Our results demonstrate that by incorporating multilingual evidence we can achieve impressive performance gains across a range of scenarios. We also found that performance improves steadily as the number of available languages increases.