33 citations · 84 across the 10 of their papers we have counts for
3 papers · 1 filter
Boosted Dense Retriever
Patrick Lewis, Barlas Oğuz, Wenhan Xiong +3
We propose DrBoost, a dense retrieval ensemble inspired by boosting. DrBoost is trained in stages: each component model is learned sequentially and specialized by focusing only on…
The Web Is Your Oyster - Knowledge-Intensive NLP against a Very Large Web Corpus
Aleksandra Piktus, Fabio Petroni, Vladimir Karpukhin +8
In order to address increasing demands of real-world applications, the research for knowledge-intensive NLP (KI-NLP) should advance by capturing the challenges of a truly open-doma…
Simple Local Attentions Remain Competitive for Long-Context Tasks
Wenhan Xiong, Barlas Oğuz, Anchit Gupta +5
Many NLP tasks require processing long contexts beyond the length limit of pretrained models. In order to scale these models to longer text sequences, many efficient long-range att…