Cross-lingual word embeddings are vector representations of words in different languages where words with similar meaning are represented by similar vectors, regardless of the language. Recent developments which construct these embeddings by aligning monolingual spaces have shown that accurate alignments can be obtained with little or no supervision. However, the focus has been on a particular controlled scenario for evaluation, and there is no strong evidence on how current state-of-the-art systems would fare with noisy text or for language pairs with major linguistic differences. In this paper we present an extensive evaluation over multiple cross-lingual embedding models, analyzing their strengths and limitations with respect to different variables such as target language, training corpora and amount of supervision. Our conclusions put in doubt the view that high-quality cross-lingual embeddings can always be learned without much supervision.

本文通过大量的评估，分析了多种跨语言嵌入模型的优劣，特别是在目标语言，训练语料库和监督程度等不同方面的限制，从而对“高质量跨语言嵌入模型可以在不需要太多监督的情况下学习到”的观点提出了质疑。

无监督和半监督跨语言词嵌入学习的鲁棒性研究