Good quality monolingual word embeddings (MWEs) can be built for languages
which have large amounts of unlabeled text. MWEs can be aligned to bilingual
spaces using only a few thousand word translation pairs. For low resource
languages training MWEs monolingually results in MWEs of poor quality, and thus
poor bilingual word embeddings (BWEs) as well. This paper proposes a new
approach for building BWEs in which the vector space of the high resource
source language is used as a starting point for training an embedding space for
the low resource target language. By using the source vectors as anchors the
vector spaces are automatically aligned during training. We experiment on
English-German, English-Hiligaynon and English-Macedonian. We show that our
approach results not only in improved BWEs and bilingual lexicon induction
performance, but also in improved target language MWE quality as measured using
monolingual word similarity.

提出了一种新的构建双语词向量嵌入的方法，该方法利用高资源源语言的向量空间作为训练低资源目标语言嵌入空间的起点，并通过使用源向量作为锚点，在训练过程中自动对齐向量空间，结果表明该方法不仅提高了双语词向量的质量和双语词汇词典归纳的性能，而且提高了目标语言的单语词相似性。