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Dheeraj Rajagopal
Dheeraj Rajagopal
Research Scientist
Verified email at google.com - Homepage
Title
Cited by
Cited by
Year
SenticNet 3: a common and common-sense knowledge base for cognition-driven sentiment analysis
E Cambria, D Olsher, D Rajagopal
Proceedings of the AAAI conference on artificial intelligence 28 (1), 2014
5852014
Gated-attention architectures for task-oriented language grounding
DS Chaplot, KM Sathyendra, RK Pasumarthi, D Rajagopal, ...
Proceedings of the AAAI Conference on Artificial Intelligence 32 (1), 2018
1732018
Big social data analysis
E Cambria, D Rajagopal, D Olsher, D Das
Big data computing 13, 401-414, 2013
1222013
A graph-based approach to commonsense concept extraction and semantic similarity detection
D Rajagopal, E Cambria, D Olsher, K Kwok
Proceedings of the 22nd International Conference on World Wide Web, 565-570, 2013
1212013
Generating questions and multiple-choice answers using semantic analysis of texts
J Araki, D Rajagopal, S Sankaranarayanan, S Holm, Y Yamakawa, ...
Proceedings of COLING 2016, the 26th International Conference on …, 2016
872016
Simple and effective semi-supervised question answering
B Dhingra, D Pruthi, D Rajagopal
Proceedings of the 2018 Conference of the North {A}merican Chapter of the …, 2018
832018
Selfexplain: A self-explaining architecture for neural text classifiers
D Rajagopal, V Balachandran, E Hovy, Y Tsvetkov
Proceedings of the 2021 Conference on Empirical Methods in Natural Language …, 2021
672021
A dataset for tracking entities in open domain procedural text
N Tandon, K Sakaguchi, BD Mishra, D Rajagopal, P Clark, M Guerquin, ...
Proceedings of the 2020 Conference on Empirical Methods in Natural Language …, 2020
482020
Structsum: Incorporating latent and explicit sentence dependencies for single document summarization
V Balachandran, A Pagnoni, JY Lee, D Rajagopal, J Carbonell, ...
Proceedings of the 16th Conference of the European Chapter of the …, 2021
32*2021
Commonsense-based topic modeling
D Rajagopal, D Olsher, E Cambria, K Kwok
Proceedings of the second international workshop on issues of sentiment …, 2013
312013
GECKA: game engine for commonsense knowledge acquisition
E Cambria, D Rajagopal, K Kwok, J Sepulveda
The Twenty-Eighth International Flairs Conference, 2015
272015
Think about it! Improving defeasible reasoning by first modeling the question scenario
A Madaan, N Tandon, D Rajagopal, P Clark, Y Yang, E Hovy
Proceedings of the 2021 Conference on Empirical Methods in Natural Language …, 2021
222021
What-if I ask you to explain: Explaining the effects of perturbations in procedural text
D Rajagopal, N Tandon, B Dalvi, P Clark, E Hovy
Findings of the Association for Computational Linguistics: EMNLP 2020, 3345 …, 2020
202020
Eigen: Event influence generation using pre-trained language models
A Madaan, D Rajagopal, Y Yang, A Ravichander, E Hovy, S Prabhumoye
arXiv preprint arXiv:2010.11764, 2020
162020
Counterfactual data augmentation improves factuality of abstractive summarization
D Rajagopal, S Shakeri, CN Santos, E Hovy, CC Chang
arXiv preprint arXiv:2205.12416, 2022
122022
How Far Can We Extract Diverse Perspectives from Large Language Models? Criteria-Based Diversity Prompting!
SA Hayati, M Lee, D Rajagopal, D Kang
arXiv preprint arXiv:2311.09799, 2023
112023
Automix: Automatically mixing language models
P Aggarwal, A Madaan, A Anand, SP Potharaju, S Mishra, P Zhou, ...
arXiv preprint arXiv:2310.12963, 2023
112023
Could you give me a hint? Generating inference graphs for defeasible reasoning
A Madaan, D Rajagopal, N Tandon, Y Yang, E Hovy
Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021 …, 2021
112021
Conditional set generation using seq2seq models
A Madaan, D Rajagopal, N Tandon, Y Yang, A Bosselut
arXiv preprint arXiv:2205.12485, 2022
82022
Modeling the relationship between user comments and edits in document revision
X Zhang, D Rajagopal, M Gamon, SK Jauhar, CT Lu
Proceedings of the 2019 Conference on Empirical Methods in Natural Language …, 2019
82019
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