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Swabha Swayamdipta
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2020 – today
- 2024
- [c40]Xinyue Cui, Swabha Swayamdipta:
Annotating FrameNet via Structure-Conditioned Language Generation. ACL (Short Papers) 2024: 681-692 - [c39]Sayan Ghosh, Tejas Srinivasan, Swabha Swayamdipta:
Compare without Despair: Reliable Preference Evaluation with Generation Separability. EMNLP (Findings) 2024: 12787-12805 - [c38]Aryan Gulati, Xingjian Dong, Carlos Hurtado, Sarath Shekkizhar, Swabha Swayamdipta, Antonio Ortega:
Out-of-Distribution Detection through Soft Clustering with Non-Negative Kernel Regression. EMNLP (Findings) 2024: 12943-12959 - [c37]Jaspreet Ranjit, Brihi Joshi, Rebecca Dorn, Laura Petry, Olga Koumoundouros, Jayne Bottarini, Peichen Liu, Eric Rice, Swabha Swayamdipta:
OATH-Frames: Characterizing Online Attitudes Towards Homelessness with LLM Assistants. EMNLP 2024: 13033-13059 - [c36]Yoonsoo Nam, Adam Lehavi, Daniel Yang, Digbalay Bose, Swabha Swayamdipta, Shrikanth Narayanan:
Does Video Summarization Require Videos? Quantifying the Effectiveness of Language in Video Summarization. ICASSP 2024: 8396-8400 - [c35]Matthew Finlayson, John Hewitt, Alexander Koller, Swabha Swayamdipta, Ashish Sabharwal:
Closing the Curious Case of Neural Text Degeneration. ICLR 2024 - [c34]Amirmohammad Nazari
, Souti Chattopadhyay
NomNom: Explanatory Function Names for Program Synthesizers. ICSE Companion 2024: 418-419 - [c33]Phillip Howard, Junlin Wang, Vasudev Lal, Gadi Singer, Yejin Choi, Swabha Swayamdipta:
NeuroComparatives: Neuro-Symbolic Distillation of Comparative Knowledge. NAACL-HLT (Findings) 2024: 4502-4520 - [c32]Amirmohammad Nazari, Swabha Swayamdipta, Souti Chattopadhyay, Mukund Raghothaman:
Generating Function Names to Improve Comprehension of Synthesized Programs. VL/HCC 2024: 248-259 - [e2]Yang (Trista) Cao, Isabel Papadimitriou, Anaelia Ovalle, Marcos Zampieri, Francis Ferraro, Swabha Swayamdipta:
Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Student Research Workshop, NAACL 2024, Mexico City, Mexico, June 18, 2024. Association for Computational Linguistics 2024, ISBN 979-8-89176-117-9 [contents] - [i40]Amirmohammad Nazari, Souti Chattopadhyay, Swabha Swayamdipta, Mukund Raghothaman:
Generative Explanations for Program Synthesizers. CoRR abs/2403.03429 (2024) - [i39]Matthew Finlayson, Xiang Ren, Swabha Swayamdipta:
Logits of API-Protected LLMs Leak Proprietary Information. CoRR abs/2403.09539 (2024) - [i38]Xinyue Cui, Swabha Swayamdipta:
Annotating FrameNet via Structure-Conditioned Language Generation. CoRR abs/2406.04834 (2024) - [i37]Jaspreet Ranjit, Brihi Joshi, Rebecca Dorn, Laura Petry, Olga Koumoundouros, Jayne Bottarini, Peichen Liu, Eric Rice, Swabha Swayamdipta:
OATH-Frames: Characterizing Online Attitudes Towards Homelessness with LLM Assistants. CoRR abs/2406.14883 (2024) - [i36]Sayan Ghosh, Tejas Srinivasan, Swabha Swayamdipta:
Compare without Despair: Reliable Preference Evaluation with Generation Separability. CoRR abs/2407.01878 (2024) - [i35]Aryan Gulati, Xingjian Dong, Carlos Hurtado, Sarath Shekkizhar, Swabha Swayamdipta, Antonio Ortega:
Out-of-Distribution Detection through Soft Clustering with Non-Negative Kernel Regression. CoRR abs/2407.13141 (2024) - [i34]Urja Khurana, Eric T. Nalisnick, Antske Fokkens, Swabha Swayamdipta:
Crowd-Calibrator: Can Annotator Disagreement Inform Calibration in Subjective Tasks? CoRR abs/2408.14141 (2024) - 2023
- [j1]Krishna Pillutla, Lang Liu, John Thickstun, Sean Welleck, Swabha Swayamdipta, Rowan Zellers, Sewoong Oh, Yejin Choi, Zaïd Harchaoui:
MAUVE Scores for Generative Models: Theory and Practice. J. Mach. Learn. Res. 24: 356:1-356:92 (2023) - [c31]Hanjie Chen, Faeze Brahman, Xiang Ren, Yangfeng Ji, Yejin Choi, Swabha Swayamdipta:
REV: Information-Theoretic Evaluation of Free-Text Rationales. ACL (1) 2023: 2007-2030 - [c30]Xuhui Zhou, Hao Zhu, Akhila Yerukola, Thomas Davidson, Jena D. Hwang, Swabha Swayamdipta, Maarten Sap:
COBRA Frames: Contextual Reasoning about Effects and Harms of Offensive Statements. ACL (Findings) 2023: 6294-6315 - [c29]Chandra Bhagavatula, Jena D. Hwang, Doug Downey, Ronan Le Bras, Ximing Lu, Lianhui Qin, Keisuke Sakaguchi, Swabha Swayamdipta, Peter West, Yejin Choi:
I2D2: Inductive Knowledge Distillation with NeuroLogic and Self-Imitation. ACL (1) 2023: 9614-9630 - [c28]Alisa Liu, Zhaofeng Wu, Julian Michael, Alane Suhr, Peter West, Alexander Koller, Swabha Swayamdipta, Noah A. Smith, Yejin Choi:
We're Afraid Language Models Aren't Modeling Ambiguity. EMNLP 2023: 790-807 - [i33]Alisa Liu, Zhaofeng Wu, Julian Michael, Alane Suhr, Peter West, Alexander Koller, Swabha Swayamdipta, Noah A. Smith, Yejin Choi:
We're Afraid Language Models Aren't Modeling Ambiguity. CoRR abs/2304.14399 (2023) - [i32]Phillip Howard, Junlin Wang, Vasudev Lal, Gadi Singer, Yejin Choi, Swabha Swayamdipta:
NeuroComparatives: Neuro-Symbolic Distillation of Comparative Knowledge. CoRR abs/2305.04978 (2023) - [i31]Xuhui Zhou, Hao Zhu, Akhila Yerukola, Thomas Davidson, Jena D. Hwang, Swabha Swayamdipta, Maarten Sap:
COBRA Frames: Contextual Reasoning about Effects and Harms of Offensive Statements. CoRR abs/2306.01985 (2023) - [i30]Yoonsoo Nam, Adam Lehavi, Daniel Yang, Digbalay Bose, Swabha Swayamdipta, Shrikanth Narayanan:
Does Video Summarization Require Videos? Quantifying the Effectiveness of Language in Video Summarization. CoRR abs/2309.09405 (2023) - [i29]Matthew Finlayson, John Hewitt, Alexander Koller, Swabha Swayamdipta, Ashish Sabharwal:
Closing the Curious Case of Neural Text Degeneration. CoRR abs/2310.01693 (2023) - 2022
- [c27]Phillip Howard, Gadi Singer, Vasudev Lal, Yejin Choi, Swabha Swayamdipta:
NeuroCounterfactuals: Beyond Minimal-Edit Counterfactuals for Richer Data Augmentation. EMNLP (Findings) 2022: 5056-5072 - [c26]Jiao Sun, Swabha Swayamdipta, Jonathan May, Xuezhe Ma:
Investigating the Benefits of Free-Form Rationales. EMNLP (Findings) 2022: 5867-5882 - [c25]Alisa Liu, Swabha Swayamdipta, Noah A. Smith, Yejin Choi:
WANLI: Worker and AI Collaboration for Natural Language Inference Dataset Creation. EMNLP (Findings) 2022: 6826-6847 - [c24]Kawin Ethayarajh, Yejin Choi, Swabha Swayamdipta:
Understanding Dataset Difficulty with V-Usable Information. ICML 2022: 5988-6008 - [c23]Sarah Wiegreffe, Jack Hessel, Swabha Swayamdipta, Mark O. Riedl, Yejin Choi:
Reframing Human-AI Collaboration for Generating Free-Text Explanations. NAACL-HLT 2022: 632-658 - [c22]Maarten Sap, Swabha Swayamdipta, Laura Vianna, Xuhui Zhou, Yejin Choi, Noah A. Smith:
Annotators with Attitudes: How Annotator Beliefs And Identities Bias Toxic Language Detection. NAACL-HLT 2022: 5884-5906 - [i28]Alisa Liu, Swabha Swayamdipta, Noah A. Smith, Yejin Choi:
WANLI: Worker and AI Collaboration for Natural Language Inference Dataset Creation. CoRR abs/2201.05955 (2022) - [i27]Jiao Sun, Swabha Swayamdipta, Jonathan May, Xuezhe Ma:
Investigating the Benefits of Free-Form Rationales. CoRR abs/2206.11083 (2022) - [i26]Hanjie Chen, Faeze Brahman, Xiang Ren, Yangfeng Ji, Yejin Choi, Swabha Swayamdipta:
REV: Information-Theoretic Evaluation of Free-Text Rationales. CoRR abs/2210.04982 (2022) - [i25]Phillip Howard, Gadi Singer, Vasudev Lal, Yejin Choi, Swabha Swayamdipta:
NeuroCounterfactuals: Beyond Minimal-Edit Counterfactuals for Richer Data Augmentation. CoRR abs/2210.12365 (2022) - [i24]Chandra Bhagavatula, Jena D. Hwang, Doug Downey, Ronan Le Bras, Ximing Lu, Keisuke Sakaguchi, Swabha Swayamdipta, Peter West, Yejin Choi:
I2D2: Inductive Knowledge Distillation with NeuroLogic and Self-Imitation. CoRR abs/2212.09246 (2022) - [i23]Krishna Pillutla, Lang Liu, John Thickstun, Sean Welleck, Swabha Swayamdipta, Rowan Zellers, Sewoong Oh, Yejin Choi, Zaïd Harchaoui:
MAUVE Scores for Generative Models: Theory and Practice. CoRR abs/2212.14578 (2022) - 2021
- [c21]Alisa Liu, Maarten Sap, Ximing Lu, Swabha Swayamdipta, Chandra Bhagavatula, Noah A. Smith, Yejin Choi:
DExperts: Decoding-Time Controlled Text Generation with Experts and Anti-Experts. ACL/IJCNLP (1) 2021: 6691-6706 - [c20]Xuhui Zhou, Maarten Sap, Swabha Swayamdipta, Yejin Choi, Noah A. Smith:
Challenges in Automated Debiasing for Toxic Language Detection. EACL 2021: 3143-3155 - [c19]Alon Jacovi, Swabha Swayamdipta, Shauli Ravfogel, Yanai Elazar, Yejin Choi, Yoav Goldberg:
Contrastive Explanations for Model Interpretability. EMNLP (1) 2021: 1597-1611 - [c18]Krishna Pillutla, Swabha Swayamdipta, Rowan Zellers, John Thickstun, Sean Welleck, Yejin Choi, Zaïd Harchaoui:
MAUVE: Measuring the Gap Between Neural Text and Human Text using Divergence Frontiers. NeurIPS 2021: 4816-4828 - [i22]Xuhui Zhou, Maarten Sap, Swabha Swayamdipta, Noah A. Smith, Yejin Choi:
Challenges in Automated Debiasing for Toxic Language Detection. CoRR abs/2102.00086 (2021) - [i21]Krishna Pillutla, Swabha Swayamdipta, Rowan Zellers, John Thickstun, Yejin Choi, Zaïd Harchaoui:
MAUVE: Human-Machine Divergence Curves for Evaluating Open-Ended Text Generation. CoRR abs/2102.01454 (2021) - [i20]Alon Jacovi, Swabha Swayamdipta, Shauli Ravfogel, Yanai Elazar, Yejin Choi, Yoav Goldberg:
Contrastive Explanations for Model Interpretability. CoRR abs/2103.01378 (2021) - [i19]Alisa Liu, Maarten Sap, Ximing Lu, Swabha Swayamdipta, Chandra Bhagavatula, Noah A. Smith, Yejin Choi:
On-the-Fly Controlled Text Generation with Experts and Anti-Experts. CoRR abs/2105.03023 (2021) - [i18]Ayush Pancholy, Miriam R. L. Petruck, Swabha Swayamdipta:
Sister Help: Data Augmentation for Frame-Semantic Role Labeling. CoRR abs/2109.07725 (2021) - [i17]Kawin Ethayarajh, Yejin Choi, Swabha Swayamdipta:
Information-Theoretic Measures of Dataset Difficulty. CoRR abs/2110.08420 (2021) - [i16]Maarten Sap, Swabha Swayamdipta, Laura Vianna, Xuhui Zhou, Yejin Choi, Noah A. Smith:
Annotators with Attitudes: How Annotator Beliefs And Identities Bias Toxic Language Detection. CoRR abs/2111.07997 (2021) - [i15]Sarah Wiegreffe, Jack Hessel, Swabha Swayamdipta, Mark O. Riedl, Yejin Choi:
Reframing Human-AI Collaboration for Generating Free-Text Explanations. CoRR abs/2112.08674 (2021) - 2020
- [c17]Roy Schwartz, Gabriel Stanovsky, Swabha Swayamdipta, Jesse Dodge, Noah A. Smith:
The Right Tool for the Job: Matching Model and Instance Complexities. ACL 2020: 6640-6651 - [c16]Suchin Gururangan, Ana Marasovic, Swabha Swayamdipta, Kyle Lo, Iz Beltagy, Doug Downey, Noah A. Smith:
Don't Stop Pretraining: Adapt Language Models to Domains and Tasks. ACL 2020: 8342-8360 - [c15]Yiben Yang, Chaitanya Malaviya, Jared Fernandez, Swabha Swayamdipta, Ronan Le Bras, Ji-Ping Wang, Chandra Bhagavatula, Yejin Choi, Doug Downey:
G-DAug: Generative Data Augmentation for Commonsense Reasoning. EMNLP (Findings) 2020: 1008-1025 - [c14]Swabha Swayamdipta, Roy Schwartz, Nicholas Lourie, Yizhong Wang, Hannaneh Hajishirzi, Noah A. Smith, Yejin Choi:
Dataset Cartography: Mapping and Diagnosing Datasets with Training Dynamics. EMNLP (1) 2020: 9275-9293 - [c13]Ronan Le Bras, Swabha Swayamdipta, Chandra Bhagavatula, Rowan Zellers, Matthew E. Peters, Ashish Sabharwal, Yejin Choi:
Adversarial Filters of Dataset Biases. ICML 2020: 1078-1088 - [i14]Ronan Le Bras, Swabha Swayamdipta, Chandra Bhagavatula, Rowan Zellers, Matthew E. Peters, Ashish Sabharwal, Yejin Choi:
Adversarial Filters of Dataset Biases. CoRR abs/2002.04108 (2020) - [i13]Roy Schwartz, Gabi Stanovsky, Swabha Swayamdipta, Jesse Dodge, Noah A. Smith:
The Right Tool for the Job: Matching Model and Instance Complexities. CoRR abs/2004.07453 (2020) - [i12]Suchin Gururangan, Ana Marasovic, Swabha Swayamdipta, Kyle Lo, Iz Beltagy, Doug Downey
Don't Stop Pretraining: Adapt Language Models to Domains and Tasks. CoRR abs/2004.10964 (2020) - [i11]Yiben Yang, Chaitanya Malaviya, Jared Fernandez, Swabha Swayamdipta, Ronan Le Bras, Ji-Ping Wang, Chandra Bhagavatula, Yejin Choi, Doug Downey
G-DAUG: Generative Data Augmentation for Commonsense Reasoning. CoRR abs/2004.11546 (2020) - [i10]Swabha Swayamdipta, Roy Schwartz, Nicholas Lourie, Yizhong Wang, Hannaneh Hajishirzi, Noah A. Smith, Yejin Choi:
Dataset Cartography: Mapping and Diagnosing Datasets with Training Dynamics. CoRR abs/2009.10795 (2020)
2010 – 2019
- 2019
- [c12]Sebastian Ruder, Matthew E. Peters, Swabha Swayamdipta, Thomas Wolf:
Transfer Learning in Natural Language Processing. NAACL-HLT (Tutorial Abstracts) 2019: 15-18 - [e1]Colin Cherry, Greg Durrett, George F. Foster, Reza Haffari, Shahram Khadivi, Nanyun Peng, Xiang Ren, Swabha Swayamdipta:
Proceedings of the 2nd Workshop on Deep Learning Approaches for Low-Resource NLP, DeepLo@EMNLP-IJCNLP 2019, Hong Kong, China, November 3, 2019. Association for Computational Linguistics 2019, ISBN 978-1-950737-78-9 [contents] - [i9]Swabha Swayamdipta, Matthew E. Peters, Brendan Roof, Chris Dyer, Noah A. Smith:
Shallow Syntax in Deep Water. CoRR abs/1908.11047 (2019) - 2018
- [c11]Phoebe Mulcaire, Swabha Swayamdipta
Polyglot Semantic Role Labeling. ACL (2) 2018: 667-672 - [c10]Collin F. Baker, Michael Ellsworth, Miriam R. L. Petruck, Swabha Swayamdipta:
Frame Semantics across Languages: Towards a Multilingual FrameNet. COLING (Tutorials) 2018: 9-12 - [c9]Swabha Swayamdipta, Sam Thomson, Kenton Lee, Luke Zettlemoyer, Chris Dyer, Noah A. Smith:
Syntactic Scaffolds for Semantic Structures. EMNLP 2018: 3772-3782 - [c8]Swabha Swayamdipta
Multi-Mention Learning for Reading Comprehension with Neural Cascades. ICLR (Poster) 2018 - [c7]Suchin Gururangan, Swabha Swayamdipta, Omer Levy, Roy Schwartz, Samuel R. Bowman, Noah A. Smith:
Annotation Artifacts in Natural Language Inference Data. NAACL-HLT (2) 2018: 107-112 - [c6]Hao Peng, Sam Thomson, Swabha Swayamdipta, Noah A. Smith:
Learning Joint Semantic Parsers from Disjoint Data. NAACL-HLT 2018: 1492-1502 - [i8]Suchin Gururangan, Swabha Swayamdipta, Omer Levy, Roy Schwartz, Samuel R. Bowman, Noah A. Smith:
Annotation Artifacts in Natural Language Inference Data. CoRR abs/1803.02324 (2018) - [i7]Hao Peng, Sam Thomson, Swabha Swayamdipta, Noah A. Smith:
Learning Joint Semantic Parsers from Disjoint Data. CoRR abs/1804.05990 (2018) - [i6]Phoebe Mulcaire, Swabha Swayamdipta, Noah A. Smith:
Polyglot Semantic Role Labeling. CoRR abs/1805.11598 (2018) - [i5]Swabha Swayamdipta, Sam Thomson, Kenton Lee, Luke Zettlemoyer, Chris Dyer, Noah A. Smith:
Syntactic Scaffolds for Semantic Structures. CoRR abs/1808.10485 (2018) - 2017
- [i4]Graham Neubig, Chris Dyer, Yoav Goldberg, Austin Matthews, Waleed Ammar, Antonios Anastasopoulos, Miguel Ballesteros, David Chiang, Daniel Clothiaux, Trevor Cohn, Kevin Duh, Manaal Faruqui, Cynthia Gan, Dan Garrette, Yangfeng Ji, Lingpeng Kong, Adhiguna Kuncoro, Gaurav Kumar, Chaitanya Malaviya, Paul Michel, Yusuke Oda, Matthew Richardson, Naomi Saphra, Swabha Swayamdipta, Pengcheng Yin:
DyNet: The Dynamic Neural Network Toolkit. CoRR abs/1701.03980 (2017) - [i3]Swabha Swayamdipta, Sam Thomson, Chris Dyer, Noah A. Smith:
Frame-Semantic Parsing with Softmax-Margin Segmental RNNs and a Syntactic Scaffold. CoRR abs/1706.09528 (2017) - [i2]Swabha Swayamdipta, Ankur P. Parikh, Tom Kwiatkowski:
Multi-Mention Learning for Reading Comprehension with Neural Cascades. CoRR abs/1711.00894 (2017) - 2016
- [c5]Swabha Swayamdipta, Miguel Ballesteros, Chris Dyer, Noah A. Smith:
Greedy, Joint Syntactic-Semantic Parsing with Stack LSTMs. CoNLL 2016: 187-197 - [i1]Swabha Swayamdipta, Miguel Ballesteros, Chris Dyer, Noah A. Smith:
Greedy, Joint Syntactic-Semantic Parsing with Stack LSTMs. CoRR abs/1606.08954 (2016) - 2014
- [c4]Lingpeng Kong, Nathan Schneider, Swabha Swayamdipta, Archna Bhatia, Chris Dyer, Noah A. Smith:
A Dependency Parser for Tweets. EMNLP 2014: 1001-1012 - [c3]Sam Thomson, Brendan O'Connor, Jeffrey Flanigan
CMU: Arc-Factored, Discriminative Semantic Dependency Parsing. SemEval@COLING 2014: 176-180 - [c2]Austin Matthews, Waleed Ammar, Archna Bhatia, Weston Feely, Greg Hanneman, Eva Schlinger, Swabha Swayamdipta, Yulia Tsvetkov, Alon Lavie, Chris Dyer:
The CMU Machine Translation Systems at WMT 2014. WMT@ACL 2014: 142-149 - 2012
- [c1]Swabha Swayamdipta, Owen Rambow:
The Pursuit of Power and Its Manifestation in Written Dialog. ICSC 2012: 22-29
Coauthor Index
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