Contrastively trained vision-language models have achieved remarkable
pr...
We study speech-to-speech translation (S2ST) that translates speech from...
We present SpeechMatrix, a large-scale multilingual corpus of
speech-to-...
Pre-trained masked language models successfully perform few-shot learnin...
Prior work on language model pre-training has explored different
archite...
Self-supervised pretraining has made few-shot learning possible for many...
Mixture of Experts layers (MoEs) enable efficient scaling of language mo...
Large-scale autoregressive language models such as GPT-3 are few-shot
le...
Recent work has demonstrated the effectiveness of cross-lingual language...
State-of-the-art natural language understanding classification models fo...
Unsupervised pre-training has led to much recent progress in natural lan...
We propose a simple and efficient multi-hop dense retrieval approach for...
The state of the art on many NLP tasks is currently achieved by large
pr...
Recent breakthroughs of pretrained language models have shown the
effect...
Language model pretraining has led to significant performance gains but
...
Traditional language models are unable to efficiently model entity names...