38 citations · 159 across the 19 of their papers we have counts for
6 papers · 1 filter
Enhancing the Ranking Context of Dense Retrieval Methods through Reciprocal Nearest Neighbors
George Zerveas, Navid Rekabsaz, Carsten Eickhoff
Sparse annotation poses persistent challenges to training dense retrieval models; for example, it distorts the training signal when unlabeled relevant documents are used spuriously…
Wasserstein Adversarial Learning based Temporal Knowledge Graph Embedding
Yuanfei Dai, Wenzhong Guo, Carsten Eickhoff
Research on knowledge graph embedding (KGE) has emerged as an active field in which most existing KGE approaches mainly focus on static structural data and ignore the influence of…
CODER: An efficient framework for improving retrieval through COntextual Document Embedding Reranking
George Zerveas, Navid Rekabsaz, Daniel Cohen +1
Contrastive learning has been the dominant approach to training dense retrieval models. In this work, we investigate the impact of ranking context - an often overlooked aspect of l…
A Modern Perspective on Query Likelihood with Deep Generative Retrieval Models
Oleg Lesota, Navid Rekabsaz, Daniel Cohen +3
Existing neural ranking models follow the text matching paradigm, where document-to-query relevance is estimated through predicting the matching score. Drawing from the rich litera…
Not All Relevance Scores are Equal: Efficient Uncertainty and Calibration Modeling for Deep Retrieval Models
Daniel Cohen, Bhaskar Mitra, Oleg Lesota +2
In any ranking system, the retrieval model outputs a single score for a document based on its belief on how relevant it is to a given search query. While retrieval models have cont…
TripClick: The Log Files of a Large Health Web Search Engine
Navid Rekabsaz, Oleg Lesota, Markus Schedl +2
Click logs are valuable resources for a variety of information retrieval (IR) tasks. This includes query understanding/analysis, as well as learning effective IR models particularl…