Title: ECHO: Where Multilingual Sentence Embeddings Speak the Same Language
Primary area: unsupervised, self-supervised, semi-supervised, and supervised representation learning
Keywords: sentence embeddings, multilinguality, cross-lingual transfer

Abstract: Cross-lingual sentence encoders create unified embedding representations of sentences across languages. However, achieving both strong downstream performance and cross-lingual alignment remains a fundamental challenge. Early models relied on contrastive learning, yet were unable to leverage hard negatives to unlock the full benefits of the contrastive paradigm. These contrastive approaches were surpassed by non-contrastive approaches leveraging token-level decoders. This is in contrast with recent generic embedding models that achieve strong results by combining contrastive objectives, large language models (LLMs) initialization, and hard negatives usage.
We introduce ECHO, a novel cross-lingual sentence encoder that bridges this gap by integrating pretrained LLMs in an Encoder-Decoder architecture with contrastive training and hard negatives. Our bottleneck Encoder-Decoder design forces the model to capture essential semantic information in a shared vector space while preserving fine-grained nuances. ECHO achieves half of the error rate of the previous state-of-the-art encoders in cross-lingual similarity search across 200 languages, while showcasing unprecedented cross-lingual transfer on downstream tasks.