  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.
