49 citations · 140 across the 16 of their papers we have counts for
7 papers · 1 filter
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…
Efficient Neural Ranking using Forward Indexes and Lightweight Encoders
Jurek Leonhardt, Henrik Müller, Koustav Rudra +3
Dual-encoder-based dense retrieval models have become the standard in IR. They employ large Transformer-based language models, which are notoriously inefficient in terms of resourc…
Supervised Contrastive Learning Approach for Contextual Ranking
Abhijit Anand, Jurek Leonhardt, Koustav Rudra +1
Contextual ranking models have delivered impressive performance improvements over classical models in the document ranking task. However, these highly over-parameterized models ten…
MTLTS: A Multi-Task Framework To Obtain Trustworthy Summaries From Crisis-Related Microblogs
Rajdeep Mukherjee, Uppada Vishnu, Hari Chandana Peruri +4
Occurrences of catastrophes such as natural or man-made disasters trigger the spread of rumours over social media at a rapid pace. Presenting a trustworthy and summarized account o…
Efficient Neural Ranking using Forward Indexes
Jurek Leonhardt, Koustav Rudra, Megha Khosla +2
Neural document ranking approaches, specifically transformer models, have achieved impressive gains in ranking performance. However, query processing using such over-parameterized…
Extractive Explanations for Interpretable Text Ranking
Jurek Leonhardt, Koustav Rudra, Avishek Anand
Neural document ranking models perform impressively well due to superior language understanding gained from pre-training tasks. However, due to their complexity and large number of…