Search Results for author: Vishwajeet Kumar

Found 19 papers, 8 papers with code

WARM: A Weakly (+Semi) Supervised Math Word Problem Solver

1 code implementation COLING 2022 Oishik Chatterjee, Isha Pandey, Aashish Waikar, Vishwajeet Kumar, Ganesh Ramakrishnan

In order to address this challenge of equation annotation, we propose a weakly supervised model for solving MWPs by requiring only the final answer as supervision.

Math

PrimeQA: The Prime Repository for State-of-the-Art Multilingual Question Answering Research and Development

1 code implementation23 Jan 2023 Avirup Sil, Jaydeep Sen, Bhavani Iyer, Martin Franz, Kshitij Fadnis, Mihaela Bornea, Sara Rosenthal, Scott McCarley, Rong Zhang, Vishwajeet Kumar, Yulong Li, Md Arafat Sultan, Riyaz Bhat, Radu Florian, Salim Roukos

The field of Question Answering (QA) has made remarkable progress in recent years, thanks to the advent of large pre-trained language models, newer realistic benchmark datasets with leaderboards, and novel algorithms for key components such as retrievers and readers.

Question Answering Reading Comprehension +1

Multi-Row, Multi-Span Distant Supervision For Table+Text Question

no code implementations14 Dec 2021 Vishwajeet Kumar, Yash Gupta, Saneem Chemmengath, Jaydeep Sen, Soumen Chakrabarti, Samarth Bharadwaj, Feifei Pan

Question answering (QA) over tables and linked text, also called TextTableQA, has witnessed significant research in recent years, as tables are often found embedded in documents along with related text.

Question Answering Reading Comprehension

Topic Transferable Table Question Answering

1 code implementation EMNLP 2021 Saneem Ahmed Chemmengath, Vishwajeet Kumar, Samarth Bharadwaj, Jaydeep Sen, Mustafa Canim, Soumen Chakrabarti, Alfio Gliozzo, Karthik Sankaranarayanan

Weakly-supervised table question-answering(TableQA) models have achieved state-of-art performance by using pre-trained BERT transformer to jointly encoding a question and a table to produce structured query for the question.

Question Answering Question Generation +1

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

WARM: A Weakly (+Semi) Supervised Model for Solving Math word Problems

no code implementations14 Apr 2021 Oishik Chatterjee, Isha Pandey, Aashish Waikar, Vishwajeet Kumar, Ganesh Ramakrishnan

We approach this problem by first learning to generate the equation using the problem description and the final answer, which we subsequently use to train a supervised MWP solver.

Math

Question Generation from Paragraphs: A Tale of Two Hierarchical Models

no code implementations8 Nov 2019 Vishwajeet Kumar, Raktim Chaki, Sai Teja Talluri, Ganesh Ramakrishnan, Yuan-Fang Li, Gholamreza Haffari

Specifically, we propose (a) a novel hierarchical BiLSTM model with selective attention and (b) a novel hierarchical Transformer architecture, both of which learn hierarchical representations of paragraphs.

Question Generation Question-Generation +2

Putting the Horse before the Cart: A Generator-Evaluator Framework for Question Generation from Text

no code implementations CONLL 2019 Vishwajeet Kumar, Ganesh Ramakrishnan, Yuan-Fang Li

The \textit{generator} is a sequence-to-sequence model that incorporates the \textit{structure} and \textit{semantics} of the question being generated.

Question Generation Question-Generation

Putting the Horse Before the Cart:A Generator-Evaluator Framework for Question Generation from Text

no code implementations15 Aug 2018 Vishwajeet Kumar, Ganesh Ramakrishnan, Yuan-Fang Li

The {\it generator} is a sequence-to-sequence model that incorporates the {\it structure} and {\it semantics} of the question being generated.

Question Generation Question-Generation

Entity Resolution and Location Disambiguation in the Ancient Hindu Temples Domain using Web Data

no code implementations NAACL 2018 Ayush Maheshwari, Vishwajeet Kumar, Ganesh Ramakrishnan, J. Saketha Nath

We present a system for resolving entities and disambiguating locations based on publicly available web data in the domain of ancient Hindu Temples.

Clustering Entity Resolution

Civique: Using Social Media to Detect Urban Emergencies

no code implementations14 Oct 2016 Diptesh Kanojia, Vishwajeet Kumar, Krithi Ramamritham

We present the Civique system for emergency detection in urban areas by monitoring micro blogs like Tweets.

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