Distributed representations of words which map each word to a continuous
vector have proven useful in capturing important linguistic information not
only in a single language but also across different languages. Current
unsupervised adversarial approaches show that it is possible to build a mapping
matrix that align two sets of monolingual word embeddings together without high
quality parallel data such as a dictionary or a sentence-aligned corpus.
However, without post refinement, the performance of these methods' preliminary
mapping is not good, leading to poor performance for typologically distant
languages.
In this paper, we propose a weakly-supervised adversarial training method to
overcome this limitation, based on the intuition that mapping across languages
is better done at the concept level than at the word level. We propose a
concept-based adversarial training method which for most languages improves the
performance of previous unsupervised adversarial methods, especially for
typologically distant language pairs.

本论文提出了一种基于概念的弱监督对抗训练方法，通过将单词映射到连续向量的分布表示法，实现不需要大量平行数据（如字典或句子对齐语料库）的语言间互译，并针对语言间距离大的情况，在大多数语言上改善了以前无监督的对抗方法的性能。