Recent research on cross-lingual word embeddings has been dominated by
unsupervised mapping approaches that align monolingual embeddings. Such methods
critically rely on those embeddings having a similar structure, but it was
recently shown that the separate training in different languages causes
departures from this assumption. In this paper, we propose an alternative
approach that does not have this limitation, while requiring a weak seed
dictionary (e.g., a list of identical words) as the only form of supervision.
Rather than aligning two fixed embedding spaces, our method works by fixing the
target language embeddings, and learning a new set of embeddings for the source
language that are aligned with them. To that end, we use an extension of
skip-gram that leverages translated context words as anchor points, and
incorporates self-learning and iterative restarts to reduce the dependency on
the initial dictionary. Our approach outperforms conventional mapping methods
on bilingual lexicon induction, and obtains competitive results in the
downstream XNLI task.

本研究提出了一种基于弱监督（仅有相同单词列表）的方法，通过固定目标语言的嵌入并学习与之对齐的源语言的嵌入来解决不同语言的单词嵌入相似性不一致的问题，并在双语词表归纳和 XNLI 任务上取得了较好的结果，相比于传统的映射方法表现更好。