Numerous recent work on unsupervised machine translation (UMT) implies that
competent unsupervised translations of low-resource and unrelated languages,
such as Nepali or Sinhala, are only possible if the model is trained in a
massive multilingual environment, where these low-resource languages are mixed
with high-resource counterparts. Nonetheless, while the high-resource languages
greatly help kick-start the target low-resource translation tasks, the language
discrepancy between them may hinder their further improvement. In this work, we
propose a simple refinement procedure to separate languages from a pre-trained
multilingual UMT model for it to focus on only the target low-resource task.
Our method achieves the state of the art in the fully unsupervised translation
tasks of English to Nepali, Sinhala, Gujarati, Latvian, Estonian and Kazakh,
with BLEU score gains of 3.5, 3.5, 3.3, 4.1, 4.2, and 3.3, respectively. Our
codebase is available at
this https URL

该论文提出一种简单的细化流程，将语言从预训练的多语言 UMT 模型中分离出来，使其专注于目标低资源任务，并实现了英语到尼泊尔语、僧伽罗语、古吉拉特语、拉脱维亚语、爱沙尼亚语和哈萨克语的全无监督翻译任务的最先进，其 BLEU 得分分别为 3.5，3.5，3.3，4.1，4.2 和 3.3。

通过语言分离多语言模型提高低资源无监督翻译的精度

Refining Low-Resource Unsupervised Translation by Language Disentanglement of Multilingual Model

Complex Word Identification (CWI) is a task centered on detecting
hard-to-understand words, or groups of words, in texts from different areas of
expertise. The purpose of CWI is to highlight problematic structures that
non-native speakers would usually find difficult to understand. Our approach
uses zero-shot, one-shot, and few-shot learning techniques, alongside
state-of-the-art solutions for Natural Language Processing (NLP) tasks (i.e.,
Transformers). Our aim is to provide evidence that the proposed models can
learn the characteristics of complex words in a multilingual environment by
relying on the CWI shared task 2018 dataset available for four different
languages (i.e., English, German, Spanish, and also French). Our approach
surpasses state-of-the-art cross-lingual results in terms of macro F1-score on
English (0.774), German (0.782), and Spanish (0.734) languages, for the
zero-shot learning scenario. At the same time, our model also outperforms the
state-of-the-art monolingual result for German (0.795 macro F1-score).

本研究利用零样本、一次样本和少量样本学习技术，结合自然语言处理的最先进解决方案，通过使用四种不同语言（英语、德语、西班牙语和法语）的 CWI 共享任务数据集，证明了提出的模型可以在多语言环境中学习复杂单词的特征，并在零样本学习场景下在英语、德语和西班牙语三种语言中的宏 F1 分数上超过了现有的跨语言结果。同时，我们的模型也在德语（0.795 宏 F1 分数）的单语上表现出色。