Hessel, Jack

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

OLMo: Accelerating the Science of Language Models

Groeneveld, D., Beltagy, I., Walsh, E., Bhagia, A., Kinney, R., Tafjord, O., Jha, A., Ivison, H., Magnusson, I., Wang, Y., et al. (2024) · Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Topic ModelingNatural Language Processing Techniques

Cited by 70

SODA: Million-scale Dialogue Distillation with Social Commonsense Contextualization

Kim, H., Hessel, J., Jiang, L., West, P., Lu, X., Yu, Y., Zhou, P., Bras, R., Alikhani, M., Kim, G., et al. (2023) · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing

Speech and dialogue systemsTopic Modeling

Cited by 63

Does my multimodal model learn cross-modal interactions? It’s harder to tell than you might think!

Hessel, J., & Lee, L. (2020) · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

Multimodal Machine Learning ApplicationsMultisensory perception and integration

Cited by 57

Is Reinforcement Learning (Not) for Natural Language Processing?: Benchmarks, Baselines, and Building Blocks for Natural Language Policy Optimization

Ramamurthy, R., Ammanabrolu, P., Brantley, K., Hessel, J., Sifa, R., Bauckhage, C., Hajishirzi, H., & Choi, Y. (2022) · arXiv

Reinforcement Learning in RoboticsNatural Language Processing Techniques

Cited by 56

Breaking Common Sense: WHOOPS! A Vision-and-Language Benchmark of Synthetic and Compositional Images

Bitton-Guetta, N., Bitton, Y., Hessel, J., Schmidt, L., Elovici, Y., Stanovsky, G., & Schwartz, R. (2023) · 2023 IEEE/CVF International Conference on Computer Vision (ICCV)

Multimodal Machine Learning ApplicationsGenerative Adversarial Networks and Image Synthesis

Cited by 37

Multimodal C4: An Open, Billion-scale Corpus of Images Interleaved With Text

Zhu, W., Hessel, J., Awadalla, A., Gadre, S. Y., Dodge, J., Fang, A., Yu, Y., Schmidt, L., Wang, W. Y., & Choi, Y. (2023) · arXiv

Multimodal Machine Learning ApplicationsNatural Language Processing Techniques

Cited by 21

OLMo: Accelerating the Science of Language Models

Groeneveld, D., Beltagy, I., Walsh, P., Bhagia, A., Kinney, R., Tafjord, O., Jha, A. H., Ivison, H., Magnusson, I., Wang, Y., et al. (2024) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 10

WildChat: 1M ChatGPT Interaction Logs in the Wild

Zhao, W., Ren, X., Hessel, J., Cardie, C., Choi, Y., & Deng, Y. (2024) · arXiv

Speech and dialogue systemsNatural Language Processing Techniques

Cited by 10

SODA: Million-scale Dialogue Distillation with Social Commonsense Contextualization

Kim, H., Hessel, J., Jiang, L., West, P., Lu, X., Yu, Y., Zhou, P., Bras, R. L., Alikhani, M., Kim, G., et al. (2022) · arXiv

Speech and dialogue systemsTopic Modeling

Cited by 3