241 citations · 276 across the 4 of their papers we have counts for
14 papers
Multi-task Retrieval for Knowledge-Intensive Tasks
Jean Maillard, Vladimir Karpukhin, Fabio Petroni +4
Retrieving relevant contexts from a large corpus is a crucial step for tasks such as open-domain question answering and fact checking. Although neural retrieval outperforms traditi…
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…
Pretrained Encyclopedia: Weakly Supervised Knowledge-Pretrained Language Model
Wenhan Xiong, Jingfei Du, William Yang Wang +1
Recent breakthroughs of pretrained language models have shown the effectiveness of self-supervised learning for a wide range of natural language processing (NLP) tasks. In addition…
SemEval-2013 Task 2: Sentiment Analysis in Twitter
Preslav Nakov, Zornitsa Kozareva, Alan Ritter +3
In recent years, sentiment analysis in social media has attracted a lot of research interest and has been used for a number of applications. Unfortunately, research has been hinder…
SemEval-2014 Task 9: Sentiment Analysis in Twitter
Sara Rosenthal, Preslav Nakov, Alan Ritter +1
We describe the Sentiment Analysis in Twitter task, ran as part of SemEval-2014. It is a continuation of the last year's task that ran successfully as part of SemEval-2013. As in 2…
SemEval-2015 Task 10: Sentiment Analysis in Twitter
Sara Rosenthal, Saif M Mohammad, Preslav Nakov +3
In this paper, we describe the 2015 iteration of the SemEval shared task on Sentiment Analysis in Twitter. This was the most popular sentiment analysis shared task to date with mor…