We introduce BilBOWA (Bilingual Bag-of-Words without Alignments), a simple
and computationally-efficient model for learning bilingual distributed
representations of words which can scale to large monolingual datasets and does
not require word-aligned parallel training data. Instead it trains directly on
monolingual data and extracts a bilingual signal from a smaller set of raw-text
sentence-aligned data. This is achieved using a novel sampled bag-of-words
cross-lingual objective, which is used to regularize two noise-contrastive
language models for efficient cross-lingual feature learning. We show that
bilingual embeddings learned using the proposed model outperform
state-of-the-art methods on a cross-lingual document classification task as
well as a lexical translation task on WMT11 data.

BilBOWA 是一种简单、高效的双语词袋模型，可以在大型单语数据集上进行规模化学习，并且不需要单词对齐的平行训练数据，通过使用一种新颖的跨语言词袋采样方法实现双语信号的提取，使用噪声对比性语言模型进行规范化处理，学习到的双语嵌入能够在跨语言文档分类任务和 WMT11 数据的词汇翻译任务上优于现有的方法。