We present Emu, a system that semantically enhances multilingual sentence
embeddings. Our framework fine-tunes pre-trained multilingual sentence
embeddings using two main components: a semantic classifier and a language
discriminator. The semantic classifier improves the semantic similarity of
related sentences, whereas the language discriminator enhances the
multilinguality of the embeddings via multilingual adversarial training. Our
experimental results based on several language pairs show that our specialized
embeddings outperform the state-of-the-art multilingual sentence embedding
model on the task of cross-lingual intent classification using only monolingual
labeled data.

Emu 是一个可在多语言中加强句子语义的系统，其利用语义分类器和语言鉴别器两大主要组件对预训练的多语言句子嵌入进行微调，以提高相关句子的语义相似度和通过多语言对抗式训练来增强多语言性能，并经多种语言对的实验结果表明，在仅使用单语标记数据的情况下，我们专门的嵌入模型在跨语言意图分类任务上优于最先进的多语言句子嵌入模型。