This paper investigates an unsupervised approach towards deriving a
universal, cross-lingual word embedding space, where words with similar
semantics from different languages are close to one another. Previous
adversarial approaches have shown promising results in inducing cross-lingual
word embedding without parallel data. However, the training stage shows
instability for distant language pairs. Instead of mapping the source language
space directly to the target language space, we propose to make use of a
sequence of intermediate spaces for smooth bridging. Each intermediate space
may be conceived as a pseudo-language space and is introduced via simple linear
interpolation. This approach is modeled after domain flow in computer vision,
but with a modified objective function. Experiments on intrinsic Bilingual
Dictionary Induction tasks show that the proposed approach can improve the
robustness of adversarial models with comparable and even better precision.
Further experiments on the downstream task of Cross-Lingual Natural Language
Inference show that the proposed model achieves significant performance
improvement for distant language pairs in downstream tasks compared to
state-of-the-art adversarial and non-adversarial models.

该论文探讨了一种无监督的方法，用于推导出一种通用的，跨语言的词嵌入空间，其中不同语言中具有类似语义的单词彼此接近。通过使用线性插值的一系列中间空间，该方法模拟了计算机视觉中的域流方法，以提高跨语言自然语言推理的性能。