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Dinesh Khandelwal
Dinesh Khandelwal
IBM Research LAB India
Dirección de correo verificada de in.ibm.com - Página principal
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Leveraging abstract meaning representation for knowledge base question answering
P Kapanipathi, I Abdelaziz, S Ravishankar, S Roukos, A Gray, R Astudillo, ...
arXiv preprint arXiv:2012.01707, 2020
117*2020
Explanations for commonsenseqa: New dataset and models
S Aggarwal, D Mandowara, V Agrawal, D Khandelwal, P Singla, D Garg
Proceedings of the 59th Annual Meeting of the Association for Computational …, 2021
1012021
The techqa dataset
V Castelli, R Chakravarti, S Dana, A Ferritto, R Florian, M Franz, D Garg, ...
arXiv preprint arXiv:1911.02984, 2019
292019
Translucent answer predictions in multi-hop reading comprehension
GPS Bhargav, M Glass, D Garg, S Shevade, S Dana, D Khandelwal, ...
Proceedings of the AAAI conference on artificial intelligence 34 (05), 7700-7707, 2020
132020
Sygma: System for generalizable modular question answering overknowledge bases
S Neelam, U Sharma, H Karanam, S Ikbal, P Kapanipathi, I Abdelaziz, ...
arXiv preprint arXiv:2109.13430, 2021
122021
A deep neural approach to KGQA via SPARQL silhouette generation
S Purkayastha, S Dana, D Garg, D Khandelwal, GPS Bhargav
2022 International Joint Conference on Neural Networks (IJCNN), 1-8, 2022
92022
A benchmark for generalizable and interpretable temporal question answering over knowledge bases
S Neelam, U Sharma, H Karanam, S Ikbal, P Kapanipathi, I Abdelaziz, ...
arXiv preprint arXiv:2201.05793, 2022
92022
Lazy Generic Cuts
Dinesh Khandelwal, Kush Bhatia, Chetan Arora, Parag Singla
Computer Vision and Image Understanding 143, 80-91, 2016
9*2016
Fill in the blank: Exploring and enhancing llm capabilities for backward reasoning in math word problems
A Deb, N Oza, S Singla, D Khandelwal, D Garg, P Singla
arXiv preprint arXiv:2310.01991, 2023
62023
Knowledge graph question answering via SPARQL silhouette generation
S Purkayastha, S Dana, D Garg, D Khandelwal, GP Bhargav
arXiv preprint arXiv:2109.09475, 2021
62021
Avi Sil, Rosario Uceda-Sosa, Todd Ward, and Rong Zhang. 2020. The TechQA dataset
V Castelli, R Chakravarti, S Dana, A Ferritto, R Florian, M Franz, D Garg, ...
Proceedings of the 58th Annual Meeting of the Association for Computational …, 0
6
Inductive quantum embedding
SK Srivastava, D Khandelwal, D Madan, D Garg, H Karanam, ...
Advances in Neural Information Processing Systems 33, 16012-16024, 2020
52020
Max-margin feature selection
Y Prasad, D Khandelwal, KK Biswas
Pattern Recognition Letters 95, 51-57, 2017
52017
Zero-shot entity linking with less data
GPS Bhargav, D Khandelwal, S Dana, D Garg, P Kapanipathi, S Roukos, ...
Findings of the Association for Computational Linguistics: NAACL 2022, 1681-1697, 2022
32022
Targeted extraction of temporal facts from textual resources for improved temporal question answering over knowledge bases
N Kannen, U Sharma, S Neelam, D Khandelwal, S Ikbal, H Karanam, ...
arXiv preprint arXiv:2203.11054, 2022
12022
Semantic Answer Type Prediction using Dense Type Embeddings
GPS Bhargav, D Khandelwal, D Garg, S Dana
SeMantic Answer Type and Relation Prediction Task at ISWC Semantic Web Challenge, 2021
12021
Exploiting test time evidence to improve predictions of deep neural networks
D Khandelwal, S Agrawal, P Singla, C Arora
CoRR, 2018
12018
Knowledge graph question answering with neural machine translation
D Saswati, D Garg, D Khandelwal, GPS Bhargav, S Purkayastha
US Patent App. 17/810,013, 2024
2024
Automated fact checking using iterative knowledge base querying
GPS Bhargav, D Saswati, D Khandelwal, D Garg
US Patent App. 17/747,463, 2023
2023
Best of Both Worlds: Towards Improving Temporal Knowledge Base Question Answering via Targeted Fact Extraction
N Kannen, U Sharma, S Neelam, D Khandelwal, S Ikbal, H Karanam, ...
Proceedings of the 2023 Conference on Empirical Methods in Natural Language …, 2023
2023
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