Search Results for author: Rishav Chakravarti

Found 12 papers, 3 papers with code

Capturing Row and Column Semantics in Transformer Based Question Answering over Tables

1 code implementation NAACL 2021 Michael Glass, Mustafa Canim, Alfio Gliozzo, Saneem Chemmengath, Vishwajeet Kumar, Rishav Chakravarti, Avi Sil, Feifei Pan, Samarth Bharadwaj, Nicolas Rodolfo Fauceglia

While this model yields extremely high accuracy at finding cell values on recent benchmarks, a second model we propose, called RCI representation, provides a significant efficiency advantage for online QA systems over tables by materializing embeddings for existing tables.

Question Answering

Towards Confident Machine Reading Comprehension

no code implementations20 Jan 2021 Rishav Chakravarti, Avirup Sil

Performance prediction is particularly important in cases of domain shift (as measured by training RC models on SQUAD 2. 0 and evaluating on NQ), where Mr. C not only improves AUC, but also traditional answerability prediction (as measured by a 5 point improvement in F1).

Machine Reading Comprehension Question Answering

Towards building a Robust Industry-scale Question Answering System

no code implementations COLING 2020 Rishav Chakravarti, Anthony Ferritto, Bhavani Iyer, Lin Pan, Radu Florian, Salim Roukos, Avi Sil

Building on top of the powerful BERTQA model, GAAMA provides a ∼2. 0{\%} absolute boost in F1 over the industry-scale state-of-the-art (SOTA) system on NQ.

Data Augmentation Question Answering +1

ARES: A Reading Comprehension Ensembling Service

no code implementations EMNLP 2020 Anthony Ferritto, Lin Pan, Rishav Chakravarti, Salim Roukos, Radu Florian, J. William Murdock, Avi Sil

We introduce ARES (A Reading Comprehension Ensembling Service): a novel Machine Reading Comprehension (MRC) demonstration system which utilizes an ensemble of models to increase F1 by 2. 3 points.

Machine Reading Comprehension Question Answering

Ensembling Strategies for Answering Natural Questions

no code implementations30 Oct 2019 Anthony Ferritto, Lin Pan, Rishav Chakravarti, Salim Roukos, Radu Florian, J. William Murdock, Avirup Sil

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 Questions (NQ) challenges.

Question Answering

Structured Pruning of a BERT-based Question Answering Model

no code implementations14 Oct 2019 J. S. McCarley, Rishav Chakravarti, Avirup Sil

The recent trend in industry-setting Natural Language Processing (NLP) research has been to operate large %scale pretrained language models like BERT under strict computational limits.

Model Compression Pretrained Language Models +1

Frustratingly Easy Natural Question Answering

no code implementations11 Sep 2019 Lin Pan, Rishav Chakravarti, Anthony Ferritto, Michael Glass, Alfio Gliozzo, Salim Roukos, Radu Florian, Avirup Sil

Existing literature on Question Answering (QA) mostly focuses on algorithmic novelty, data augmentation, or increasingly large pre-trained language models like XLNet and RoBERTa.

Data Augmentation Question Answering +1

Span Selection Pre-training for Question Answering

1 code implementation ACL 2020 Michael Glass, Alfio Gliozzo, Rishav Chakravarti, Anthony Ferritto, Lin Pan, G P Shrivatsa Bhargav, Dinesh Garg, Avirup Sil

BERT (Bidirectional Encoder Representations from Transformers) and related pre-trained Transformers have provided large gains across many language understanding tasks, achieving a new state-of-the-art (SOTA).

Language Modelling Question Answering +1

CFO: A Framework for Building Production NLP Systems

no code implementations IJCNLP 2019 Rishav Chakravarti, Cezar Pendus, Andrzej Sakrajda, Anthony Ferritto, Lin Pan, Michael Glass, Vittorio Castelli, J. William Murdock, Radu Florian, Salim Roukos, Avirup Sil

This paper introduces a novel orchestration framework, called CFO (COMPUTATION FLOW ORCHESTRATOR), for building, experimenting with, and deploying interactive NLP (Natural Language Processing) and IR (Information Retrieval) systems to production environments.

Information Retrieval Machine Reading Comprehension +1

A Study on Passage Re-ranking in Embedding based Unsupervised Semantic Search

no code implementations22 Apr 2018 Md. Faisal Mahbub Chowdhury, Vijil Chenthamarakshan, Rishav Chakravarti, Alfio M. Gliozzo

State of the art approaches for (embedding based) unsupervised semantic search exploits either compositional similarity (of a query and a passage) or pair-wise word (or term) similarity (from the query and the passage).

Passage Re-Ranking Re-Ranking +2

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