Recipe for Zero-shot POS Tagging: Is It Useful in Realistic Scenarios?

TL;DR

This paper investigates the effectiveness of zero-shot POS tagging for low-resource languages using multilingual models, emphasizing the importance of dataset quality and linguistic relatedness for optimal results.

Contribution

It introduces a zero-shot POS tagging approach for low-resource languages and analyzes how dataset quality and linguistic relationships influence performance.

Findings

Zero-shot models perform well for extremely low-resource languages.

Linguistic relatedness improves zero-shot POS tagging accuracy.

High-quality datasets are crucial for effective zero-shot tagging.

Abstract

POS tagging plays a fundamental role in numerous applications. While POS taggers are highly accurate in well-resourced settings, they lag behind in cases of limited or missing training data. This paper focuses on POS tagging for languages with limited data. We seek to identify the characteristics of datasets that make them favourable for training POS tagging models without using any labelled training data from the target language. This is a zero-shot approach. We compare the accuracies of a multilingual large language model (mBERT) fine-tuned on one or more languages related to the target language. Additionally, we compare these results with models trained directly on the target language itself. We do this for three target low-resource languages. Our research highlights the importance of accurate dataset selection for effective zero-shot POS tagging. Particularly, a strong linguistic relationship and high-quality datasets ensure optimal results. For extremely low-resource languages, zero-shot models prove to be a viable option.