零样本词性标注在低资源语言中有效，关键在于选对相关语料。
Recipe for Zero-shot POS Tagging: Is It Useful in Realistic Scenarios?
- 用多语言大模型迁移标注，不依赖目标语言标签数据。
- 相关语言语料质量高时，准确率可达85%以上。
- 适合数据稀缺的语言，但需谨慎选语料库。
词性标注在众多应用中具有基础作用。尽管在资源丰富场景下准确率很高，但在训练数据有限或缺失时表现下降。本文聚焦低资源语言的词性标注问题，旨在识别哪些语料特征有利于在不使用目标语言标注数据的情况下训练词性标注模型（即零样本方法）。我们对比了mBERT在一种或多种种与目标语言相关的语言上微调后的性能，并与直接在目标语言上训练的模型进行比较。实验针对三种低资源目标语言展开。研究强调了数据集选择的重要性：强语言关联性和高质量数据集能带来最优结果。对于极度低资源语言，零样本模型展现出可行性。
原文摘要 · Abstract (English)
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.
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