# Emu: Enhancing Multilingual Sentence Embeddings with Semantic Specialization

arXiv: https://arxiv.org/abs/1909.06731 · HTML: https://mlsanity.com/paper/1909.06731/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Sep 2019.

## Built on by (most cited first, 1 of 1)

- [Leveraging Multi-lingual Positive Instances in Contrastive Learning to Improve Sentence Embedding](https://mlsanity.com/paper/2309.08929/) (2023)

## Abstract (arXiv)

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.