@inproceedings{chen-etal-2021-nice-neural,
title = "{NICE}: Neural Image Commenting with Empathy",
author = "Chen, Kezhen and
Huang, Qiuyuan and
McDuff, Daniel and
Gao, Xiang and
Palangi, Hamid and
Wang, Jianfeng and
Forbus, Kenneth and
Gao, Jianfeng",
editor = "Moens, Marie-Francine and
Huang, Xuanjing and
Specia, Lucia and
Yih, Scott Wen-tau",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2021",
month = nov,
year = "2021",
address = "Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.findings-emnlp.380",
doi = "10.18653/v1/2021.findings-emnlp.380",
pages = "4456--4472",
abstract = "Emotion and empathy are examples of human qualities lacking in many human-machine interactions. The goal of our work is to generate engaging dialogue grounded in a user-shared image with increased emotion and empathy while minimizing socially inappropriate or offensive outputs. We release the Neural Image Commenting with Empathy (NICE) dataset consisting of almost two million images and the corresponding human-generated comments, a set of human annotations, and baseline performance on a range of models. In-stead of relying on manually labeled emotions, we also use automatically generated linguistic representations as a source of weakly supervised labels. Based on these annotations, we define two different tasks for the NICE dataset. Then, we propose a novel pre-training model - Modeling Affect Generation for Image Comments (MAGIC) - which aims to generate comments for images, conditioned on linguistic representations that capture style and affect, and to help generate more empathetic, emotional, engaging and socially appropriate comments. Using this model we achieve state-of-the-art performance on one of our NICE tasks. The experiments show that the approach can generate more human-like and engaging image comments.",
}
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<abstract>Emotion and empathy are examples of human qualities lacking in many human-machine interactions. The goal of our work is to generate engaging dialogue grounded in a user-shared image with increased emotion and empathy while minimizing socially inappropriate or offensive outputs. We release the Neural Image Commenting with Empathy (NICE) dataset consisting of almost two million images and the corresponding human-generated comments, a set of human annotations, and baseline performance on a range of models. In-stead of relying on manually labeled emotions, we also use automatically generated linguistic representations as a source of weakly supervised labels. Based on these annotations, we define two different tasks for the NICE dataset. Then, we propose a novel pre-training model - Modeling Affect Generation for Image Comments (MAGIC) - which aims to generate comments for images, conditioned on linguistic representations that capture style and affect, and to help generate more empathetic, emotional, engaging and socially appropriate comments. Using this model we achieve state-of-the-art performance on one of our NICE tasks. The experiments show that the approach can generate more human-like and engaging image comments.</abstract>
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%0 Conference Proceedings
%T NICE: Neural Image Commenting with Empathy
%A Chen, Kezhen
%A Huang, Qiuyuan
%A McDuff, Daniel
%A Gao, Xiang
%A Palangi, Hamid
%A Wang, Jianfeng
%A Forbus, Kenneth
%A Gao, Jianfeng
%Y Moens, Marie-Francine
%Y Huang, Xuanjing
%Y Specia, Lucia
%Y Yih, Scott Wen-tau
%S Findings of the Association for Computational Linguistics: EMNLP 2021
%D 2021
%8 November
%I Association for Computational Linguistics
%C Punta Cana, Dominican Republic
%F chen-etal-2021-nice-neural
%X Emotion and empathy are examples of human qualities lacking in many human-machine interactions. The goal of our work is to generate engaging dialogue grounded in a user-shared image with increased emotion and empathy while minimizing socially inappropriate or offensive outputs. We release the Neural Image Commenting with Empathy (NICE) dataset consisting of almost two million images and the corresponding human-generated comments, a set of human annotations, and baseline performance on a range of models. In-stead of relying on manually labeled emotions, we also use automatically generated linguistic representations as a source of weakly supervised labels. Based on these annotations, we define two different tasks for the NICE dataset. Then, we propose a novel pre-training model - Modeling Affect Generation for Image Comments (MAGIC) - which aims to generate comments for images, conditioned on linguistic representations that capture style and affect, and to help generate more empathetic, emotional, engaging and socially appropriate comments. Using this model we achieve state-of-the-art performance on one of our NICE tasks. The experiments show that the approach can generate more human-like and engaging image comments.
%R 10.18653/v1/2021.findings-emnlp.380
%U https://aclanthology.org/2021.findings-emnlp.380
%U https://doi.org/10.18653/v1/2021.findings-emnlp.380
%P 4456-4472
Markdown (Informal)
[NICE: Neural Image Commenting with Empathy](https://aclanthology.org/2021.findings-emnlp.380) (Chen et al., Findings 2021)
ACL
- Kezhen Chen, Qiuyuan Huang, Daniel McDuff, Xiang Gao, Hamid Palangi, Jianfeng Wang, Kenneth Forbus, and Jianfeng Gao. 2021. NICE: Neural Image Commenting with Empathy. In Findings of the Association for Computational Linguistics: EMNLP 2021, pages 4456–4472, Punta Cana, Dominican Republic. Association for Computational Linguistics.