25 citations · 84 across the 12 of their papers we have counts for
23 papers
Bridging the Training-Inference Gap for Dense Phrase Retrieval
Gyuwan Kim, Jinhyuk Lee, Barlas Oguz +4
Building dense retrievers requires a series of standard procedures, including training and validating neural models and creating indexes for efficient search. However, these proced…
Open-Domain Question-Answering for COVID-19 and Other Emergent Domains
Sharon Levy, Kevin Mo, Wenhan Xiong +1
Since late 2019, COVID-19 has quickly emerged as the newest biomedical domain, resulting in a surge of new information. As with other emergent domains, the discussion surrounding t…
Zero-shot Fact Verification by Claim Generation
Liangming Pan, Wenhu Chen, Wenhan Xiong +2
Neural models for automated fact verification have achieved promising results thanks to the availability of large, human-annotated datasets. However, for each new domain that requi…
Unsupervised Multi-hop Question Answering by Question Generation
Liangming Pan, Wenhu Chen, Wenhan Xiong +2
Obtaining training data for multi-hop question answering (QA) is time-consuming and resource-intensive. We explore the possibility to train a well-performed multi-hop QA model with…
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…
Progressively Pretrained Dense Corpus Index for Open-Domain Question Answering
Wenhan Xiong, Hong Wang, William Yang Wang
To extract answers from a large corpus, open-domain question answering (QA) systems usually rely on information retrieval (IR) techniques to narrow the search space. Standard inver…