45 citations · 127 across the 24 of their papers we have counts for
9 papers · 1 filter
A Benchmark for Open-Domain Numerical Fact-Checking Enhanced by Claim Decomposition
V Venktesh, Deepali Prabhu, Avishek Anand
Fact-checking numerical claims is critical as the presence of numbers provide mirage of veracity despite being fake potentially causing catastrophic impacts on society. The prior w…
FlashCheck: Exploration of Efficient Evidence Retrieval for Fast Fact-Checking
Kevin Nanekhan, Venktesh V, Erik Martin +3
The advances in digital tools have led to the rampant spread of misinformation. While fact-checking aims to combat this, manual fact-checking is cumbersome and not scalable. It is…
Understanding the User: An Intent-Based Ranking Dataset
Abhijit Anand, Jurek Leonhardt, V Venktesh +1
As information retrieval systems continue to evolve, accurate evaluation and benchmarking of these systems become pivotal. Web search datasets, such as MS MARCO, primarily provide…
Is Interpretable Machine Learning Effective at Feature Selection for Neural Learning-to-Rank?
Lijun Lyu, Nirmal Roy, Harrie Oosterhuis +1
Neural ranking models have become increasingly popular for real-world search and recommendation systems in recent years. Unlike their tree-based counterparts, neural models are muc…
The Surprising Effectiveness of Rankers Trained on Expanded Queries
Abhijit Anand, Venktesh V, Vinay Setty +1
An important problem in text-ranking systems is handling the hard queries that form the tail end of the query distribution. The difficulty may arise due to the presence of uncommon…
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…
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…
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…