77 citations · 263 across the 17 of their papers we have counts for
4 papers · 1 filter
Supervised Contrastive Learning for Pre-trained Language Model Fine-tuning
Beliz Gunel, Jingfei Du, Alexis Conneau +1
State-of-the-art natural language understanding classification models follow two-stages: pre-training a large language model on an auxiliary task, and then fine-tuning the model on…
Self-training Improves Pre-training for Natural Language Understanding
Jingfei Du, Edouard Grave, Beliz Gunel +5
Unsupervised pre-training has led to much recent progress in natural language understanding. In this paper, we study self-training as another way to leverage unlabeled data through…
Answering Complex Open-Domain Questions with Multi-Hop Dense Retrieval
Wenhan Xiong, Xiang Lorraine Li, Srini Iyer +8
We propose a simple and efficient multi-hop dense retrieval approach for answering complex open-domain questions, which achieves state-of-the-art performance on two multi-hop datas…
General Purpose Text Embeddings from Pre-trained Language Models for Scalable Inference
Jingfei Du, Myle Ott, Haoran Li +2
The state of the art on many NLP tasks is currently achieved by large pre-trained language models, which require a considerable amount of computation. We explore a setting where ma…