Abstract
Due to the scarcity of part-of-speech annotated data, existing studies on low-resource languages typically adopt unsupervised approaches for POS tagging. Among these, POS tag projection with word alignment method transfers POS tags from a high-resource source language to a low-resource target language based on parallel corpora, making it particularly suitable for low-resource language settings. However, this approach relies heavily on parallel corpora, which are often unavailable for many low-resource languages. To overcome this limitation, we propose a fully unsupervised cross-lingual part-of-speech(POS) tagging framework that relies solely on monolingual corpora by leveraging unsupervised neural machine translation(UNMT) system. This UNMT system first translates sentences from a high-resource language into a low-resource one, thereby constructing pseudo-parallel sentence pairs. Then, we train a POS tagger for the target language following the standard projection procedure based on word alignments. Moreover, we propose a multi-source projection technique to calibrate the projected POS tags on the target side, enhancing to train a more effective POS tagger. We evaluate our framework on 28 language pairs, covering four source languages (English, German, Spanish and French) and seven target languages (Afrikaans, Basque, Finnis, Indonesian, Lithuanian, Portuguese and Turkish). Experimental results show that our method can achieve performance comparable to the baseline cross-lingual POS tagger with parallel sentence pairs, and even exceeds it for certain target languages. Furthermore, our proposed multi-source projection technique further boosts performance, yielding an average improvement of 1.3% over previous methods.
Abstract (translated)
由于词性标注数据的稀缺，现有研究中针对低资源语言通常采用无监督方法进行词性标注。在这些方法中，基于平行语料库将高资源源语言的词性标签转移到低资源目标语言的方法被证明特别适合处理低资源语言环境中的问题。然而，这种方法严重依赖于平行语料库，而这种平行语料库对于许多低资源语言往往不可用。为了克服这一限制，我们提出了一种完全无监督的跨语言词性标注框架，该框架仅依靠单语文本，并利用无监督神经机器翻译（UNMT）系统。此 UNMT 系统首先将高资源语言中的句子翻译成低资源语言，从而构建伪平行句对。然后，根据单词对齐信息，我们按照标准投影流程训练目标语言的词性标注器。此外，我们提出了一种多源投影技术来校准目标侧的投影词性标签，以增强词性标注器的有效训练。 我们在28种语言对上评估了该框架的表现，包括四种源语言（英语、德语、西班牙语和法语）以及七种目标语言（南非荷兰语、巴斯克语、芬兰语、印尼语、立陶宛语、葡萄牙语和土耳其语）。实验结果表明，我们的方法能够实现与使用平行句对的基线跨语言词性标注器相媲美的性能，并且对于某些目标语言甚至超过了它的表现。此外，我们提出的多源投影技术进一步提升了整体性能，在前人工作基础上平均提高了1.3%的表现。
URL
https://arxiv.org/abs/2602.09366
