99 citations · 123 across the 6 of their papers we have counts for
9 papers
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…
Towards Robust Neural Retrieval Models with Synthetic Pre-Training
Revanth Gangi Reddy, Vikas Yadav, Md Arafat Sultan +4
Recent work has shown that commonly available machine reading comprehension (MRC) datasets can be used to train high-performance neural information retrieval (IR) systems. However,…
End-to-End QA on COVID-19: Domain Adaptation with Synthetic Training
Revanth Gangi Reddy, Bhavani Iyer, Md Arafat Sultan +5
End-to-end question answering (QA) requires both information retrieval (IR) over a large document collection and machine reading comprehension (MRC) on the retrieved passages. Rece…
Answer Span Correction in Machine Reading Comprehension
Revanth Gangi Reddy, Md Arafat Sultan, Efsun Sarioglu Kayi +3
Answer validation in machine reading comprehension (MRC) consists of verifying an extracted answer against an input context and question pair. Previous work has looked at re-assess…
Multi-Stage Pre-training for Low-Resource Domain Adaptation
Rong Zhang, Revanth Gangi Reddy, Md Arafat Sultan +7
Transfer learning techniques are particularly useful in NLP tasks where a sizable amount of high-quality annotated data is difficult to obtain. Current approaches directly adapt a…
The TechQA Dataset
Vittorio Castelli, Rishav Chakravarti, Saswati Dana +18
We introduce TechQA, a domain-adaptation question answering dataset for the technical support domain. The TechQA corpus highlights two real-world issues from the automated customer…