activity
20092024
most citedEnd-to-End QA on COVID-19: Domain Adaptation with Synthetic Training

17 citations · 52 across the 14 of their papers we have counts for

collaborators
Showing 2020Show all

5 papers · 1 filter

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

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

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…

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.CL2020

Leveraging Semantic Parsing for Relation Linking over Knowledge Bases

Nandana Mihindukulasooriya, Gaetano Rossiello, Pavan Kapanipathi +6

Knowledgebase question answering systems are heavily dependent on relation extraction and linking modules. However, the task of extracting and linking relations from text to knowle…