3 citations · 6 across the 5 of their papers we have counts for
7 papers · 1 filter
Towards Confident Machine Reading Comprehension
Rishav Chakravarti, Avirup Sil
There has been considerable progress on academic benchmarks for the Reading Comprehension (RC) task with State-of-the-Art models closing the gap with human performance on extractiv…
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…
Ensembling Strategies for Answering Natural Questions
Anthony Ferritto, Lin Pan, Rishav Chakravarti +4
Many of the top question answering systems today utilize ensembling to improve their performance on tasks such as the Stanford Question Answering Dataset (SQuAD) and Natural Questi…
Frustratingly Easy Natural Question Answering
Lin Pan, Rishav Chakravarti, Anthony Ferritto +5
Existing literature on Question Answering (QA) mostly focuses on algorithmic novelty, data augmentation, or increasingly large pre-trained language models like XLNet and RoBERTa. A…
Span Selection Pre-training for Question Answering
Michael Glass, Alfio Gliozzo, Rishav Chakravarti +5
BERT (Bidirectional Encoder Representations from Transformers) and related pre-trained Transformers have provided large gains across many language understanding tasks, achieving a…
CFO: A Framework for Building Production NLP Systems
Rishav Chakravarti, Cezar Pendus, Andrzej Sakrajda +8
This paper introduces a novel orchestration framework, called CFO (COMPUTATION FLOW ORCHESTRATOR), for building, experimenting with, and deploying interactive NLP (Natural Language…