Zero Resource Cross-Lingual Part Of Speech Tagging
Sahil Chopra
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.