45 citations · 124 across the 8 of their papers we have counts for
9 papers
QuanTemp: A real-world open-domain benchmark for fact-checking numerical claims
Venktesh V, Abhijit Anand, Avishek Anand +1
Automated fact checking has gained immense interest to tackle the growing misinformation in the digital era. Existing systems primarily focus on synthetic claims on Wikipedia, and…
Data Augmentation for Sample Efficient and Robust Document Ranking
Abhijit Anand, Jurek Leonhardt, Jaspreet Singh +2
Contextual ranking models have delivered impressive performance improvements over classical models in the document ranking task. However, these highly over-parameterized models ten…
Explainable Information Retrieval: A Survey
Avishek Anand, Lijun Lyu, Maximilian Idahl +3
Explainable information retrieval is an emerging research area aiming to make transparent and trustworthy information retrieval systems. Given the increasing use of complex machine…
SparCAssist: A Model Risk Assessment Assistant Based on Sparse Generated Counterfactuals
Zijian Zhang, Vinay Setty, Avishek Anand
We introduce SparcAssist, a general-purpose risk assessment tool for the machine learning models trained for language tasks. It evaluates models' risk by inspecting their behavior…
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…