65 citations · 211 across the 18 of their papers we have counts for
4 papers · 1 filter
Making Pre-trained Language Models Better Few-shot Learners
Tianyu Gao, Adam Fisch, Danqi Chen
The recent GPT-3 model (Brown et al., 2020) achieves remarkable few-shot performance solely by leveraging a natural-language prompt and a few task demonstrations as input context.…
Learning from Context or Names? An Empirical Study on Neural Relation Extraction
Hao Peng, Tianyu Gao, Xu Han +5
Neural models have achieved remarkable success on relation extraction (RE) benchmarks. However, there is no clear understanding which type of information affects existing RE models…
Few-shot Relation Extraction via Bayesian Meta-learning on Relation Graphs
Meng Qu, Tianyu Gao, Louis-Pascal A. C. Xhonneux +1
This paper studies few-shot relation extraction, which aims at predicting the relation for a pair of entities in a sentence by training with a few labeled examples in each relation…
More Data, More Relations, More Context and More Openness: A Review and Outlook for Relation Extraction
Xu Han, Tianyu Gao, Yankai Lin +7
Relational facts are an important component of human knowledge, which are hidden in vast amounts of text. In order to extract these facts from text, people have been working on rel…