45 citations · 127 across the 24 of their papers we have counts for
6 papers · 1 filter
FaxPlainAC: A Fact-Checking Tool Based on EXPLAINable Models with HumAn Correction in the Loop
Zijian Zhang, Koustav Rudra, Avishek Anand
Fact-checking on the Web has become the main mechanism through which we detect the credibility of the news or information. Existing fact-checkers verify the authenticity of the inf…
Towards Axiomatic Explanations for Neural Ranking Models
Michael Völske, Alexander Bondarenko, Maik Fröbe +4
Recently, neural networks have been successfully employed to improve upon state-of-the-art performance in ad-hoc retrieval tasks via machine-learned ranking functions. While neural…
Towards Benchmarking the Utility of Explanations for Model Debugging
Maximilian Idahl, Lijun Lyu, Ujwal Gadiraju +1
Post-hoc explanation methods are an important class of approaches that help understand the rationale underlying a trained model's decision. But how useful are they for an end-user…
An In-depth Analysis of Passage-Level Label Transfer for Contextual Document Ranking
Koustav Rudra, Zeon Trevor Fernando, Avishek Anand
Pre-trained contextual language models such as BERT, GPT, and XLnet work quite well for document retrieval tasks. Such models are fine-tuned based on the query-document/query-passa…
Explain and Predict, and then Predict Again
Zijian Zhang, Koustav Rudra, Avishek Anand
A desirable property of learning systems is to be both effective and interpretable. Towards this goal, recent models have been proposed that first generate an extractive explanatio…
Revisiting the Auction Algorithm for Weighted Bipartite Perfect Matchings
Megha Khosla, Avishek Anand
We study the classical weighted perfect matchings problem for bipartite graphs or sometimes referred to as the assignment problem, i.e., given a weighted bipartite graph $G = (U\cu…