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.

零资源环境中的词性标注可以是一种有效的方法，当没有标记的训练数据可用时，特别适用于资源匮乏的语言。本文研究了使用预训练的多语言大型语言模型或在零资源目标语言中将源语言标签映射并训练序列标注模型两种主要技术进行词性标注。我们通过现成的对齐模块探索了后一种方法，并训练了一个隐马尔可夫模型来预测词性标签。我们在以英语为源语言，法语、德语和西班牙语为目标语言的词性标注中评估了转移学习设置。我们的结论是，零资源语言中的映射对于预测词性标签是有益的。