Choi, Yejin

The Curious Case of Neural Text Degeneration

Holtzman, A., Buys, J., Du, L., Forbes, M., & Choi, Y. (2019) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 1,304

CLIPScore: A Reference-free Evaluation Metric for Image Captioning

Hessel, J., Holtzman, A., Forbes, M., Le Bras, R., & Choi, Y. (2021) · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

Multimodal Machine Learning ApplicationsImage Retrieval and Classification Techniques

Cited by 1,232

Finding Deceptive Opinion Spam by Any Stretch of the Imagination

Ott, M., Choi, Y., Cardie, C., & Hancock, J. T. (2011) · arXiv

Spam and Phishing DetectionMisinformation and Its Impacts

Cited by 1,008

COMET: Commonsense Transformers for Automatic Knowledge Graph Construction

Bosselut, A., Rashkin, H., Sap, M., Malaviya, C., Celikyilmaz, A., & Choi, Y. (2019) · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

Topic ModelingMultimodal Machine Learning Applications

Cited by 904

HellaSwag: Can a Machine Really Finish Your Sentence?

Zellers, R., Holtzman, A., Bisk, Y., Farhadi, A., & Choi, Y. (2019) · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

Topic ModelingNatural Language Processing Techniques

Cited by 830

The Risk of Racial Bias in Hate Speech Detection

Sap, M., Card, D., Gabriel, S., Choi, Y., & Smith, N. A. (2019) · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

Hate Speech and Cyberbullying DetectionEthics and Social Impacts of AI

Cited by 817

From Recognition to Cognition: Visual Commonsense Reasoning

Zellers, R., Bisk, Y., Farhadi, A., & Choi, Y. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Multimodal Machine Learning ApplicationsVisual Attention and Saliency Detection

Cited by 769

QuAC: Question Answering in Context

Choi, E., He, H., Iyyer, M., Yatskar, M., Yih, W.-T., Choi, Y., Liang, P., & Zettlemoyer, L. (2018) · Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

Topic ModelingSpeech and dialogue systems

Cited by 714

RealToxicityPrompts: Evaluating Neural Toxic Degeneration in Language Models

Gehman, S., Gururangan, S., Sap, M., Choi, Y., & Smith, N. A. (2020) · Findings of the Association for Computational Linguistics: EMNLP 2020

Hate Speech and Cyberbullying DetectionAdversarial Robustness in Machine Learning

Cited by 693

WinoGrande: An Adversarial Winograd Schema Challenge at Scale

Sakaguchi, K., Le Bras, R., Bhagavatula, C., & Choi, Y. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence

Topic ModelingNatural Language Processing Techniques

Cited by 579

Defending Against Neural Fake News

Zellers, R., Holtzman, A., Rashkin, H., Bisk, Y., Farhadi, A., Roesner, F., & Choi, Y. (2019) · arXiv

Misinformation and Its ImpactsAdversarial Robustness in Machine Learning

Cited by 569

Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models

Srivastava, A., Rastogi, A., Rao, A., Shoeb, A. A. M., Abid, A., Fisch, A., Brown, A. R., Santoro, A., Gupta, A., Garriga-Alonso, A., et al. (2022) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 569

WinoGrande: an adversarial winograd schema challenge at scale

Sakaguchi, K., Bras, R. L., Bhagavatula, C., & Choi, Y. (2021) · Communications of the ACM

Topic ModelingNatural Language Processing Techniques

Cited by 356

CommonGen: A constrained text generation challenge for generative commonsense reasoning

Lin, B. Y., Zhou, W., Shen, M., Zhou, P., Bhagavatula, C., Choi, Y., & Ren, X. (2020) · Findings of the Association for Computational Linguistics: EMNLP 2020

Topic ModelingMultimodal Machine Learning Applications

Cited by 239

Learning to Write with Cooperative Discriminators

Holtzman, A., Buys, J., Forbes, M., Bosselut, A., Golub, D., & Choi, Y. (2018) · Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Topic ModelingNatural Language Processing Techniques

Cited by 192

Social chemistry 101: Learning to reason about social and moral norms

Forbes, M., Hwang, J. D., Shwartz, V., Sap, M., & Choi, Y. (2020) · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

Psychology of Moral and Emotional JudgmentTopic Modeling

Cited by 169

Surface Form Competition: Why the Highest Probability Answer Isn’t Always Right

Holtzman, A., West, P., Shwartz, V., Choi, Y., & Zettlemoyer, L. (2021) · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

Topic ModelingNatural Language Processing Techniques

Cited by 127

Is GPT-3 text indistinguishable from human text? Scarecrow: A framework for scrutinizing machine text

Dou, Y., Forbes, M., Koncel-Kedziorski, R., Smith, N., & Choi, Y. (2022) · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Natural Language Processing TechniquesTopic Modeling

Cited by 118

Neural Theory-of-Mind? On the Limits of Social Intelligence in Large LMs

Sap, M., LeBras, R., Fried, D., & Choi, Y. (2022) · arXiv

Child and Animal Learning DevelopmentTopic Modeling

Cited by 118

Neural Theory-of-Mind? On the Limits of Social Intelligence in Large LMs

Sap, M., Le Bras, R., Fried, D., & Choi, Y. (2022) · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

Child and Animal Learning DevelopmentTopic Modeling

Cited by 118

Maieutic Prompting: Logically Consistent Reasoning with Recursive Explanations

Jung, J., Qin, L., Welleck, S., Brahman, F., Bhagavatula, C., Bras, R. L., & Choi, Y. (2022) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 98

Is GPT-3 Text Indistinguishable from Human Text? Scarecrow: A Framework for Scrutinizing Machine Text

Dou, Y., Forbes, M., Koncel-Kedziorski, R., Smith, N. A., & Choi, Y. (2021) · arXiv

Natural Language Processing TechniquesTopic Modeling

Cited by 90

Contrastive Explanations for Model Interpretability

Jacovi, A., Swayamdipta, S., Ravfogel, S., Elazar, Y., Choi, Y., & Goldberg, Y. (2021) · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

Explainable Artificial Intelligence (XAI)Topic Modeling

Cited by 83

ProsocialDialog: A prosocial backbone for conversational agents

Kim, H., Yu, Y., Jiang, L., Lu, X., Khashabi, D., Kim, G., Choi, Y., & Sap, M. (2022) · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

Speech and dialogue systemsEthics and Social Impacts of AI

Cited by 80