Neural Machine Translation (NMT) has seen tremendous growth in the last ten years since
the early 2000s and has already entered a mature phase. While considered the most widely …

This paper presents E5, a family of state-of-the-art text embeddings that transfer well to a
wide range of tasks. The model is trained in a contrastive manner with weak supervision …

Text embeddings are commonly evaluated on a small set of datasets from a single task not
covering their possible applications to other tasks. It is unclear whether state-of-the-art …

Hallucinated translations can severely undermine and raise safety issues when machine
translation systems are deployed in the wild. Previous research on the topic focused on …

The new era of technology has brought us to the point where it is convenient for people to
share their opinions over an abundance of platforms. These platforms have a provision for …

It has been shown that dual encoders trained on one domain often fail to generalize to other
domains for retrieval tasks. One widespread belief is that the bottleneck layer of a dual …

We provide the first exploration of sentence embeddings from text-to-text transformers (T5).
Sentence embeddings are broadly useful for language processing tasks. While T5 achieves …

We introduce FLEURS, the Few-shot Learning Evaluation of Universal Representations of
Speech benchmark. FLEURS is an n-way parallel speech dataset in 102 languages built on …

While task-specific finetuning of pretrained networks has led to significant empirical
advances in NLP, the large size of networks makes finetuning difficult to deploy in multi-task …

In this work, we present an information-theoretic framework that formulates cross-lingual
language model pre-training as maximizing mutual information between multilingual-multi …