@inproceedings{hosseini-caragea-2021-distilling-knowledge,
title = "Distilling Knowledge for Empathy Detection",
author = "Hosseini, Mahshid and
Caragea, Cornelia",
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.314",
doi = "10.18653/v1/2021.findings-emnlp.314",
pages = "3713--3724",
abstract = "Empathy is the link between self and others. Detecting and understanding empathy is a key element for improving human-machine interaction. However, annotating data for detecting empathy at a large scale is a challenging task. This paper employs multi-task training with knowledge distillation to incorporate knowledge from available resources (emotion and sentiment) to detect empathy from the natural language in different domains. This approach yields better results on an existing news-related empathy dataset compared to strong baselines. In addition, we build a new dataset for empathy prediction with fine-grained empathy direction, seeking or providing empathy, from Twitter. We release our dataset for research purposes.",
}
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<abstract>Empathy is the link between self and others. Detecting and understanding empathy is a key element for improving human-machine interaction. However, annotating data for detecting empathy at a large scale is a challenging task. This paper employs multi-task training with knowledge distillation to incorporate knowledge from available resources (emotion and sentiment) to detect empathy from the natural language in different domains. This approach yields better results on an existing news-related empathy dataset compared to strong baselines. In addition, we build a new dataset for empathy prediction with fine-grained empathy direction, seeking or providing empathy, from Twitter. We release our dataset for research purposes.</abstract>
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%0 Conference Proceedings
%T Distilling Knowledge for Empathy Detection
%A Hosseini, Mahshid
%A Caragea, Cornelia
%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 hosseini-caragea-2021-distilling-knowledge
%X Empathy is the link between self and others. Detecting and understanding empathy is a key element for improving human-machine interaction. However, annotating data for detecting empathy at a large scale is a challenging task. This paper employs multi-task training with knowledge distillation to incorporate knowledge from available resources (emotion and sentiment) to detect empathy from the natural language in different domains. This approach yields better results on an existing news-related empathy dataset compared to strong baselines. In addition, we build a new dataset for empathy prediction with fine-grained empathy direction, seeking or providing empathy, from Twitter. We release our dataset for research purposes.
%R 10.18653/v1/2021.findings-emnlp.314
%U https://aclanthology.org/2021.findings-emnlp.314
%U https://doi.org/10.18653/v1/2021.findings-emnlp.314
%P 3713-3724
Markdown (Informal)
[Distilling Knowledge for Empathy Detection](https://aclanthology.org/2021.findings-emnlp.314) (Hosseini & Caragea, Findings 2021)
ACL
- Mahshid Hosseini and Cornelia Caragea. 2021. Distilling Knowledge for Empathy Detection. In Findings of the Association for Computational Linguistics: EMNLP 2021, pages 3713–3724, Punta Cana, Dominican Republic. Association for Computational Linguistics.