no code implementations • 14 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.
no code implementations • 7 Mar 2018 • Vishwajeet Kumar, Kireeti Boorla, Yogesh Meena, Ganesh Ramakrishnan, Yuan-Fang Li
Neural network-based methods represent the state-of-the-art in question generation from text.
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.
no code implementations • 15 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.
1 code implementation • ACL 2019 • Vishwajeet Kumar, Nitish Joshi, Arijit Mukherjee, Ganesh Ramakrishnan, Preethi Jyothi
For a new language, such training instances are hard to obtain making the QG problem even more challenging.
no code implementations • IJCNLP 2019 • Vishwajeet Kumar, Sivaanandh Muneeswaran, Ganesh Ramakrishnan, Yuan-Fang Li
Generating syntactically and semantically valid and relevant questions from paragraphs is useful with many applications.
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.
no code implementations • 8 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.
no code implementations • Asian Chapter of the Association for Computational Linguistics 2020 • Vishwajeet Kumar, Manish Joshi, Ganesh Ramakrishnan, Yuan-Fang Li
Question generation (QG) has recently attracted considerable attention.
1 code implementation • EACL 2021 • Ishan Tarunesh, Sushil Khyalia, Vishwajeet Kumar, Ganesh Ramakrishnan, Preethi Jyothi
We present experiments on five different tasks and six different languages from the XTREME multilingual benchmark dataset.
1 code implementation • 9 Mar 2021 • Aman Jain, Mayank Kothyari, Vishwajeet Kumar, Preethi Jyothi, Ganesh Ramakrishnan, Soumen Chakrabarti
In response, we identify a key structural idiom in OKVQA , viz., S3 (select, substitute and search), and build a new data set and challenge around it.
no code implementations • 14 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.
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.
1 code implementation • NAACL (ACL) 2022 • Yannis Katsis, Saneem Chemmengath, Vishwajeet Kumar, Samarth Bharadwaj, Mustafa Canim, Michael Glass, Alfio Gliozzo, Feifei Pan, Jaydeep Sen, Karthik Sankaranarayanan, Soumen Chakrabarti
Recent advances in transformers have enabled Table Question Answering (Table QA) systems to achieve high accuracy and SOTA results on open domain datasets like WikiTableQuestions and WikiSQL.
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.
no code implementations • 14 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.
1 code implementation • 23 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.
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.