27 citations · 77 across the 12 of their papers we have counts for
4 papers · 2 filters
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…
Content-Based Weak Supervision for Ad-Hoc Re-Ranking
Sean MacAvaney, Andrew Yates, Kai Hui +1
One challenge with neural ranking is the need for a large amount of manually-labeled relevance judgments for training. In contrast with prior work, we examine the use of weak super…
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…
PACRR: A Position-Aware Neural IR Model for Relevance Matching
Kai Hui, Andrew Yates, Klaus Berberich +1
In order to adopt deep learning for information retrieval, models are needed that can capture all relevant information required to assess the relevance of a document to a given use…