@inproceedings{guibon-etal-2021-meta-learning,
title = "Meta-learning for Classifying Previously Unseen Data Source into Previously Unseen Emotional Categories",
author = {Guibon, Ga{\"e}l and
Labeau, Matthieu and
Flamein, H{\'e}l{\`e}ne and
Lefeuvre, Luce and
Clavel, Chlo{\'e}},
editor = "Lee, Hung-Yi and
Mohtarami, Mitra and
Li, Shang-Wen and
Jin, Di and
Korpusik, Mandy and
Dong, Shuyan and
Vu, Ngoc Thang and
Hakkani-Tur, Dilek",
booktitle = "Proceedings of the 1st Workshop on Meta Learning and Its Applications to Natural Language Processing",
month = aug,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.metanlp-1.9/",
doi = "10.18653/v1/2021.metanlp-1.9",
pages = "76--89",
abstract = "In this paper, we place ourselves in a classification scenario in which the target classes and data type are not accessible during training. We use a meta-learning approach to determine whether or not meta-trained information from common social network data with fine-grained emotion labels can achieve competitive performance on messages labeled with different emotion categories. We leverage few-shot learning to match with the classification scenario and consider metric learning based meta-learning by setting up Prototypical Networks with a Transformer encoder, trained in an episodic fashion. This approach proves to be effective for capturing meta-information from a source emotional tag set to predict previously unseen emotional tags. Even though shifting the data type triggers an expected performance drop, our meta-learning approach achieves decent results when compared to the fully supervised one."
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<abstract>In this paper, we place ourselves in a classification scenario in which the target classes and data type are not accessible during training. We use a meta-learning approach to determine whether or not meta-trained information from common social network data with fine-grained emotion labels can achieve competitive performance on messages labeled with different emotion categories. We leverage few-shot learning to match with the classification scenario and consider metric learning based meta-learning by setting up Prototypical Networks with a Transformer encoder, trained in an episodic fashion. This approach proves to be effective for capturing meta-information from a source emotional tag set to predict previously unseen emotional tags. Even though shifting the data type triggers an expected performance drop, our meta-learning approach achieves decent results when compared to the fully supervised one.</abstract>
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%0 Conference Proceedings
%T Meta-learning for Classifying Previously Unseen Data Source into Previously Unseen Emotional Categories
%A Guibon, Gaël
%A Labeau, Matthieu
%A Flamein, Hélène
%A Lefeuvre, Luce
%A Clavel, Chloé
%Y Lee, Hung-Yi
%Y Mohtarami, Mitra
%Y Li, Shang-Wen
%Y Jin, Di
%Y Korpusik, Mandy
%Y Dong, Shuyan
%Y Vu, Ngoc Thang
%Y Hakkani-Tur, Dilek
%S Proceedings of the 1st Workshop on Meta Learning and Its Applications to Natural Language Processing
%D 2021
%8 August
%I Association for Computational Linguistics
%C Online
%F guibon-etal-2021-meta-learning
%X In this paper, we place ourselves in a classification scenario in which the target classes and data type are not accessible during training. We use a meta-learning approach to determine whether or not meta-trained information from common social network data with fine-grained emotion labels can achieve competitive performance on messages labeled with different emotion categories. We leverage few-shot learning to match with the classification scenario and consider metric learning based meta-learning by setting up Prototypical Networks with a Transformer encoder, trained in an episodic fashion. This approach proves to be effective for capturing meta-information from a source emotional tag set to predict previously unseen emotional tags. Even though shifting the data type triggers an expected performance drop, our meta-learning approach achieves decent results when compared to the fully supervised one.
%R 10.18653/v1/2021.metanlp-1.9
%U https://aclanthology.org/2021.metanlp-1.9/
%U https://doi.org/10.18653/v1/2021.metanlp-1.9
%P 76-89
Markdown (Informal)
[Meta-learning for Classifying Previously Unseen Data Source into Previously Unseen Emotional Categories](https://aclanthology.org/2021.metanlp-1.9/) (Guibon et al., MetaNLP 2021)
ACL