@inproceedings{ke-etal-2021-classic,
title = "{CLASSIC}: Continual and Contrastive Learning of Aspect Sentiment Classification Tasks",
author = "Ke, Zixuan and
Liu, Bing and
Xu, Hu and
Shu, Lei",
editor = "Moens, Marie-Francine and
Huang, Xuanjing and
Specia, Lucia and
Yih, Scott Wen-tau",
booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2021",
address = "Online and Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.emnlp-main.550",
doi = "10.18653/v1/2021.emnlp-main.550",
pages = "6871--6883",
abstract = "This paper studies continual learning (CL) of a sequence of aspect sentiment classification (ASC) tasks in a particular CL setting called domain incremental learning (DIL). Each task is from a different domain or product. The DIL setting is particularly suited to ASC because in testing the system needs not know the task/domain to which the test data belongs. To our knowledge, this setting has not been studied before for ASC. This paper proposes a novel model called CLASSIC. The key novelty is a contrastive continual learning method that enables both knowledge transfer across tasks and knowledge distillation from old tasks to the new task, which eliminates the need for task ids in testing. Experimental results show the high effectiveness of CLASSIC.",
}
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<abstract>This paper studies continual learning (CL) of a sequence of aspect sentiment classification (ASC) tasks in a particular CL setting called domain incremental learning (DIL). Each task is from a different domain or product. The DIL setting is particularly suited to ASC because in testing the system needs not know the task/domain to which the test data belongs. To our knowledge, this setting has not been studied before for ASC. This paper proposes a novel model called CLASSIC. The key novelty is a contrastive continual learning method that enables both knowledge transfer across tasks and knowledge distillation from old tasks to the new task, which eliminates the need for task ids in testing. Experimental results show the high effectiveness of CLASSIC.</abstract>
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%0 Conference Proceedings
%T CLASSIC: Continual and Contrastive Learning of Aspect Sentiment Classification Tasks
%A Ke, Zixuan
%A Liu, Bing
%A Xu, Hu
%A Shu, Lei
%Y Moens, Marie-Francine
%Y Huang, Xuanjing
%Y Specia, Lucia
%Y Yih, Scott Wen-tau
%S Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
%D 2021
%8 November
%I Association for Computational Linguistics
%C Online and Punta Cana, Dominican Republic
%F ke-etal-2021-classic
%X This paper studies continual learning (CL) of a sequence of aspect sentiment classification (ASC) tasks in a particular CL setting called domain incremental learning (DIL). Each task is from a different domain or product. The DIL setting is particularly suited to ASC because in testing the system needs not know the task/domain to which the test data belongs. To our knowledge, this setting has not been studied before for ASC. This paper proposes a novel model called CLASSIC. The key novelty is a contrastive continual learning method that enables both knowledge transfer across tasks and knowledge distillation from old tasks to the new task, which eliminates the need for task ids in testing. Experimental results show the high effectiveness of CLASSIC.
%R 10.18653/v1/2021.emnlp-main.550
%U https://aclanthology.org/2021.emnlp-main.550
%U https://doi.org/10.18653/v1/2021.emnlp-main.550
%P 6871-6883
Markdown (Informal)
[CLASSIC: Continual and Contrastive Learning of Aspect Sentiment Classification Tasks](https://aclanthology.org/2021.emnlp-main.550) (Ke et al., EMNLP 2021)
ACL