| Content planning for neural story generation with aristotelian rescoring S Goldfarb-Tarrant, T Chakrabarty, R Weischedel, N Peng Proceedings of the 2020 Conference on Empirical Methods in Natural Language …, 2020 | 139 | 2020 |
| AMPERSAND: Argument Mining for PERSuAsive oNline Discussions T Chakrabarty, C Hidey, S Muresan, K Mckeown, A Hwang EMNLP 2019 , 2933–2943, 2020 | 123 | 2020 |
| Fine-tuned Language Models are Continual Learners T Scialom, T Chakrabarty, S Muresan EMNLP 2022, 2022 | 118* | 2022 |
| COVID-Fact: Fact Extraction and Verification of Real-World Claims on COVID-19 Pandemic A Saakyan, T Chakrabarty, S Muresan Proceedings of the 59th Annual Meeting of the Association for Computational …, 2021 | 118 | 2021 |
| Art or Artifice? Large Language Models and the False Promise of Creativity T Chakrabarty, P Laban, D Agarwal, S Muresan, CS Wu CHI '24: Proceedings of the CHI Conference on Human Factors in Computing Systems, 2024 | 85 | 2024 |
| : Reverse, Retrieve, and Rank for Sarcasm Generation with Commonsense Knowledge T Chakrabarty, D Ghosh, S Muresan, N Peng ACL 2020, 7976–7986, 2020 | 81 | 2020 |
| IMHO Fine-Tuning Improves Claim Detection T Chakrabarty, C Hidey, K McKeown NAACL 2019, 558–563, 2019 | 81 | 2019 |
| MERMAID: Metaphor Generation with Symbolism and Discriminative Decoding T Chakrabarty, X Zhang, S Muresan, N Peng 2021 Annual Conference of the North American Chapter of the Association for …, 2021 | 71 | 2021 |
| FLUTE: Figurative Language Understanding through Textual Explanations T Chakrabarty, A Saakyan, D Ghosh, S Muresan EMNLP 2022, 2022 | 69* | 2022 |
| Generating similes effortlessly like a Pro: A Style Transfer Approach for Simile Generation T Chakrabarty, S Muresan, N Peng Proceedings of the 2020 Conference on Empirical Methods in Natural Language …, 2020 | 69 | 2020 |
| Help me write a poem: Instruction Tuning as a Vehicle for Collaborative Poetry Writing T Chakrabarty, V Padmakumar, H He EMNLP 2022, 2022 | 66 | 2022 |
| It’s not Rocket Science: Interpreting Figurative Language in Narratives T Chakrabarty, Y Choi, V Shwartz Transactions of the Association for Computational Linguistics 10, 589-606, 2022 | 63 | 2022 |
| Metaphor Generation with Conceptual Mappings K Stowe, T Chakrabarty, N Peng, S Muresan, I Gurevych Proceedings of the 59th Annual Meeting of the Association for Computational …, 2021 | 51 | 2021 |
| DeSePtion: Dual Sequence Prediction and Adversarial Examples for Improved Fact-Checking C Hidey, T Chakrabarty, T Alhindi, S Varia, K Krstovski, M Diab, ... ACL 2020,8593–8606, 2020 | 50 | 2020 |
| NormSAGE: Multi-Lingual Multi-Cultural Norm Discovery from Conversations On-the-Fly YR Fung, T Chakraborty, H Guo, O Rambow, S Muresan, H Ji EMNLP 2022, 2022 | 49 | 2022 |
| Pay “Attention” to your Context when Classifying Abusive Language T Chakrabarty, K Gupta, S Muresan Proceedings of the Third Workshop on Abusive Language Online, 70-79, 2019 | 48 | 2019 |
| Creativity Support in the Age of Large Language Models: An Empirical Study Involving Professional Writers T Chakrabarty, V Padmakumar, F Brahman, S Muresan C&C '24: Proceedings of the 16th Conference on Creativity & Cognition, 2024 | 46* | 2024 |
| I spy a metaphor: Large language models and diffusion models co-create visual metaphors T Chakrabarty, A Saakyan, O Winn, A Panagopoulou, Y Yang, ... ACL 2023, 2023 | 42 | 2023 |
| Figurative language in recognizing textual entailment T Chakrabarty, D Ghosh, A Poliak, S Muresan arXiv preprint arXiv:2106.01195, 2021 | 34 | 2021 |
| Multitask Instruction-based Prompting for Fallacy Recognition T Alhindi, T Chakrabarty, E Musi, S Muresan EMNLP 2022, 2022 | 29 | 2022 |
Smaranda MuresanColumbia UnviersityVerified email at ccls.columbia.edu
