| 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 | 148* | 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 | 138 | 2021 |
| The techqa dataset V Castelli, R Chakravarti, S Dana, A Ferritto, R Florian, M Franz, D Garg, ... arXiv preprint arXiv:1911.02984, 2019 | 32 | 2019 |
| 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 | 21 | 2021 |
| 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 | 13 | 2022 |
| 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 | 13 | 2020 |
| Lazy Generic Cuts Dinesh Khandelwal, Kush Bhatia, Chetan Arora, Parag Singla Computer Vision and Image Understanding 143, 80-91, 2016 | 11* | 2016 |
| 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 | 10 | 2022 |
| Knowledge graph question answering via SPARQL silhouette generation S Purkayastha, S Dana, D Garg, D Khandelwal, GP Bhargav arXiv preprint arXiv:2109.09475, 2021 | 10 | 2021 |
| 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 | 8 | 2022 |
| 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 | 8 | |
| Inductive quantum embedding SK Srivastava, D Khandelwal, D Madan, D Garg, H Karanam, ... Advances in Neural Information Processing Systems 33, 16012-16024, 2020 | 7 | 2020 |
| 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 | 6 | 2023 |
| Max-margin feature selection Y Prasad, D Khandelwal, KK Biswas Pattern Recognition Letters 95, 51-57, 2017 | 5 | 2017 |
| 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 | 4 | 2023 |
| 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 | 2 | 2022 |
| 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 | 1 | 2024 |
| 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 …, 2021 | 1 | 2021 |
| Exploiting test time evidence to improve predictions of deep neural networks D Khandelwal, S Agrawal, P Singla, C Arora CoRR, 2018 | 1 | 2018 |
| Automated fact checking using iterative knowledge base querying GPS Bhargav, D Saswati, D Khandelwal, D Garg US Patent 12,008,000, 2024 | | 2024 |
Dinesh GargIBM India Research Lab, Bangalore, IndiaVerified email at in.ibm.com
