activity
20192022
most citedEnd-to-End QA on COVID-19: Domain Adaptation with Synthetic Training

17 citations · 21 across the 3 of their papers we have counts for

collaborators

5 papers

cs.CL20221 cited

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…

cs.CL202017 cited

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…

cs.CL2020

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…

cs.CL2020

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…

cs.CL20193 cited

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…