In this work, we present a novel neural network based architecture for
inducing compositional crosslingual word representations. Unlike previously
proposed methods, our method fulfills the following three criteria; it
constrains the word-level representations to be compositional, it is capable of
leveraging both bilingual and monolingual data, and it is scalable to large
vocabularies and large quantities of data. The key component of our approach is
what we refer to as a monolingual inclusion criterion, that exploits the
observation that phrases are more closely semantically related to their
sub-phrases than to other randomly sampled phrases. We evaluate our method on a
well-established crosslingual document classification task and achieve results
that are either comparable, or greatly improve upon previous state-of-the-art
methods. Concretely, our method reaches a level of 92.7% and 84.4% accuracy for
the English to German and German to English sub-tasks respectively. The former
advances the state of the art by 0.9% points of accuracy, the latter is an
absolute improvement upon the previous state of the art by 7.7% points of
accuracy and an improvement of 33.0% in error reduction.

本文提出了一种新颖的基于神经网络的架构，用于引导组合跨语言词表示，利用双语和单语数据，通过单语包含准则来实现，评估结果超越了以前的方法，具体的，在英语到德语和德语到英语的子任务中获得了 92.7％和 84.4％的准确度，较之以前的最高值，前者提高 0.9％，后者提高了 7.7％的准确度和 33.0% 的误差降低。