仅使用单语语料的无监督跨语言词性标注
Unsupervised Cross-Lingual Part-of-Speech Tagging with Monolingual Corpora Only
AI总结：
本文提出了一种仅使用单语语料的无监督跨语言词性标注框架，通过无监督神经机器翻译构建伪平行句子对，并利用多源投影技术提升词性标注性能。
摘要：
由于词性标注数据稀缺，现有低资源语言研究通常采用无监督方法进行词性标注。其中，基于词对齐的词性标签投影方法通过平行语料将高资源源语言的词性标签转移到低资源目标语言，使其特别适合低资源语言环境。然而，这种方法依赖于平行语料，而许多低资源语言往往缺乏此类语料。为克服这一限制，我们提出了一种完全无监督的跨语言词性标注框架，仅依赖单语语料，通过利用无监督神经机器翻译（UNMT）系统实现。该UNMT系统首先将句子从高资源语言翻译成低资源语言，从而构建伪平行句子对。然后，我们按照标准投影程序基于词对齐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，以校准目标侧的投影词性标签，从而训练更有效的词性标注器。我们在28个语言对上评估了我们的框架，涵盖四种源语言（英语、德语、西班牙语和法语）和七种目标语言（阿非利卡语、巴斯克语、芬兰语、印度尼西亚语、立陶宛语、葡萄牙语和土耳其语）。实验结果表明，我们的方法可以实现与基于平行句子对的基线跨语言词性标注器相当的性能，并且在某些目标语言上甚至超过它。此外，我们提出的多源投影技术进一步提升了性能，相对于先前方法，平均提高了1.3%。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
