17 citations · 19 across the 2 of their papers we have counts for
6 papers
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…
Improved Synthetic Training for Reading Comprehension
Yanda Chen, Md Arafat Sultan, Vittorio Castelli
Automatically generated synthetic training examples have been shown to improve performance in machine reading comprehension (MRC). Compared to human annotated gold standard data, s…
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…
GPT-too: A language-model-first approach for AMR-to-text generation
Manuel Mager, Ramon Fernandez Astudillo, Tahira Naseem +4
Meaning Representations (AMRs) are broad-coverage sentence-level semantic graphs. Existing approaches to generating text from AMR have focused on training sequence-to-sequence or g…