We review motivations, definition, approaches, and methodology for
unsupervised cross-lingual learning and call for a more rigorous position in
each of them. An existing rationale for such research is based on the lack of
parallel data for many of the world's languages. However, we argue that a
scenario without any parallel data and abundant monolingual data is unrealistic
in practice. We also discuss different training signals that have been used in
previous work, which depart from the pure unsupervised setting. We then
describe common methodological issues in tuning and evaluation of unsupervised
cross-lingual models and present best practices. Finally, we provide a unified
outlook for different types of research in this area (i.e., cross-lingual word
embeddings, deep multilingual pretraining, and unsupervised machine
translation) and argue for comparable evaluation of these models.

本文回顾了无监督跨语言学习的动机、定义、方法和方法，并呼吁对每个方面进行更严格的立场。本研究的现有基础是基于世界上许多语言缺乏平行数据的事实。然而，我们认为实践中不存在不需要任何平行数据而有丰富单语数据的情况。本文还讨论了以前使用的不同训练信号，这些信号与纯无监督设置不同。然后，我们描述了无监督跨语言模型调整和评估的常见方法问题并提出最佳实践。最后，我们为这个领域中的不同类型的研究提供了统一的前景（即跨语言词嵌入，深度多语言预训练和无监督机器翻译），并主张对这些模型进行可比较的评估。