17 citations · 44 across the 20 of their papers we have counts for
6 papers · 1 filter
Moving Beyond Downstream Task Accuracy for Information Retrieval Benchmarking
Keshav Santhanam, Jon Saad-Falcon, Martin Franz +7
Neural information retrieval (IR) systems have progressed rapidly in recent years, in large part due to the release of publicly available benchmarking tasks. Unfortunately, some di…
SPARTAN: Sparse Hierarchical Memory for Parameter-Efficient Transformers
Ameet Deshpande, Md Arafat Sultan, Anthony Ferritto +3
Fine-tuning pre-trained language models (PLMs) achieves impressive performance on a range of downstream tasks, and their sizes have consequently been getting bigger. Since a differ…
GAAMA 2.0: An Integrated System that Answers Boolean and Extractive Questions
Scott McCarley, Mihaela Bornea, Sara Rosenthal +4
Recent machine reading comprehension datasets include extractive and boolean questions but current approaches do not offer integrated support for answering both question types. We…
Not to Overfit or Underfit the Source Domains? An Empirical Study of Domain Generalization in Question Answering
Md Arafat Sultan, Avirup Sil, Radu Florian
Machine learning models are prone to overfitting their training (source) domains, which is commonly believed to be the reason why they falter in novel target domains. Here we exami…
Entity-Conditioned Question Generation for Robust Attention Distribution in Neural Information Retrieval
Revanth Gangi Reddy, Md Arafat Sultan, Martin Franz +2
We show that supervised neural information retrieval (IR) models are prone to learning sparse attention patterns over passage tokens, which can result in key phrases including name…
Synthetic Target Domain Supervision for Open Retrieval QA
Revanth Gangi Reddy, Bhavani Iyer, Md Arafat Sultan +5
Neural passage retrieval is a new and promising approach in open retrieval question answering. In this work, we stress-test the Dense Passage Retriever (DPR) -- a state-of-the-art…