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.

翻译：高质量的单语言语言嵌入( MWEs) 可用于为有大量未贴标签文本的语言构建高质量的单语言嵌入(MWEs) 。 MWEs 只能使用几千个字翻译配对来调整到双语空间。 对于低资源语言的培训, MWEs 单语言语言嵌入(MWEs) 导致MWE 质量差, 因而双语语言嵌入( BWEs) 也差。 本文提出了一个新的方法, 用于建设 BWE, 将高资源源语言的矢量空间用作培训低资源目标语言嵌入空间的起点。 通过使用源矢量作为矢量的固定点, 在培训过程中自动调整矢量空间。 我们在英语- 德语、 英语- Hiligaynon 和英语- Macedonian 上进行实验。 我们显示, 我们的方法不仅在改进 BWEs 和双语词汇诱导功能方面, 而且在用单一语言相似性衡量的目标语言质量方面产生更好的目标语言 MWE 。