activity
20182022
most citedTowards Robust Neural Retrieval Models with Synthetic Pre-Training

2 citations · 4 across the 4 of their papers we have counts for

collaborators

10 papers

cs.CL2022★ 1 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.CL2021★ 1 cited

MuMuQA: Multimedia Multi-Hop News Question Answering via Cross-Media Knowledge Extraction and Grounding

Revanth Gangi Reddy, Xilin Rui, Manling Li +9

Recently, there has been an increasing interest in building question answering (QA) models that reason across multiple modalities, such as text and images. However, QA using images…

cs.CL2021★ 2 cited

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,…

cs.CL2020

Leveraging Abstract Meaning Representation for Knowledge Base Question Answering

Pavan Kapanipathi, Ibrahim Abdelaziz, Srinivas Ravishankar +27

Knowledge base question answering (KBQA)is an important task in Natural Language Processing. Existing approaches face significant challenges including complex question understandin…

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

Pushing the Limits of AMR Parsing with Self-Learning

Young-Suk Lee, Ramon Fernandez Astudillo, Tahira Naseem +3

Abstract Meaning Representation (AMR) parsing has experienced a notable growth in performance in the last two years, due both to the impact of transfer learning and the development…