7 citations · 8 across the 2 of their papers we have counts for
3 papers
cs.IR2018
Overcoming low-utility facets for complex answer retrieval
Sean MacAvaney, Andrew Yates, Arman Cohan +4
Many questions cannot be answered simply; their answers must include numerous nuanced details and additional context. Complex Answer Retrieval (CAR) is the retrieval of answers to…
cs.IR2017★ 1 cited
DE-PACRR: Exploring Layers Inside the PACRR Model
Andrew Yates, Kai Hui
Recent neural IR models have demonstrated deep learning's utility in ad-hoc information retrieval. However, deep models have a reputation for being black boxes, and the roles of a…
cs.IR2017★ 7 cited
Co-PACRR: A Context-Aware Neural IR Model for Ad-hoc Retrieval
Kai Hui, Andrew Yates, Klaus Berberich +1
Neural IR models, such as DRMM and PACRR, have achieved strong results by successfully capturing relevance matching signals. We argue that the context of these matching signals is…