19 citations · 91 across the 12 of their papers we have counts for
12 papers
Predictive Uncertainty-based Bias Mitigation in Ranking
Maria Heuss, Daniel Cohen, Masoud Mansoury +2
Societal biases that are contained in retrieved documents have received increased interest. Such biases, which are often prevalent in the training data and learned by the model, ca…
A Lightweight Constrained Generation Alternative for Query-focused Summarization
Zhichao Xu, Daniel Cohen
Query-focused summarization (QFS) aims to provide a summary of a document that satisfies information need of a given query and is useful in various IR applications, such as abstrac…
Parameter-efficient Modularised Bias Mitigation via AdapterFusion
Deepak Kumar, Oleg Lesota, George Zerveas +4
Large pre-trained language models contain societal biases and carry along these biases to downstream tasks. Current in-processing bias mitigation approaches (like adversarial train…
Metric-valued regression
Dan Tsir Cohen, Aryeh Kontorovich
We propose an efficient algorithm for learning mappings between two metric spaces, $\X$ and $\Y$. Our procedure is strongly Bayes-consistent whenever $\X$ and $\Y$ are topologicall…
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…
Evaluating the Performance of Reinforcement Learning Algorithms
Scott M. Jordan, Yash Chandak, Daniel Cohen +2
Performance evaluations are critical for quantifying algorithmic advances in reinforcement learning. Recent reproducibility analyses have shown that reported performance results ar…
Distributed Evaluations: Ending Neural Point Metrics
Daniel Cohen, Scott M. Jordan, W. Bruce Croft
With the rise of neural models across the field of information retrieval, numerous publications have incrementally pushed the envelope of performance for a multitude of IR tasks. H…
WikiPassageQA: A Benchmark Collection for Research on Non-factoid Answer Passage Retrieval
Daniel Cohen, Liu Yang, W. Bruce Croft
With the rise in mobile and voice search, answer passage retrieval acts as a critical component of an effective information retrieval system for open domain question answering. Cur…
Cross Domain Regularization for Neural Ranking Models Using Adversarial Learning
Daniel Cohen, Bhaskar Mitra, Katja Hofmann +1
Unlike traditional learning to rank models that depend on hand-crafted features, neural representation learning models learn higher level features for the ranking task by training…
Adaptability of Neural Networks on Varying Granularity IR Tasks
Daniel Cohen, Qingyao Ai, W. Bruce Croft
Recent work in Information Retrieval (IR) using Deep Learning models has yielded state of the art results on a variety of IR tasks. Deep neural networks (DNN) are capable of learni…