@inproceedings{chen-etal-2020-label,
title = "Label Representations in Modeling Classification as Text Generation",
author = "Chen, Xinyi and
Xu, Jingxian and
Wang, Alex",
editor = "Shmueli, Boaz and
Huang, Yin Jou",
booktitle = "Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing: Student Research Workshop",
month = dec,
year = "2020",
address = "Suzhou, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.aacl-srw.23",
doi = "10.18653/v1/2020.aacl-srw.23",
pages = "160--164",
abstract = "Several recent state-of-the-art transfer learning methods model classification tasks as text generation, where labels are represented as strings for the model to generate. We investigate the effect that the choice of strings used to represent labels has on how effectively the model learns the task. For four standard text classification tasks, we design a diverse set of possible string representations for labels, ranging from canonical label definitions to random strings. We experiment with T5 on these tasks, varying the label representations as well as the amount of training data. We find that, in the low data setting, label representation impacts task performance on some tasks, with task-related labels being most effective, but fails to have an impact on others. In the full data setting, our results are largely negative: Different label representations do not affect overall task performance.",
}
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%0 Conference Proceedings
%T Label Representations in Modeling Classification as Text Generation
%A Chen, Xinyi
%A Xu, Jingxian
%A Wang, Alex
%Y Shmueli, Boaz
%Y Huang, Yin Jou
%S Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing: Student Research Workshop
%D 2020
%8 December
%I Association for Computational Linguistics
%C Suzhou, China
%F chen-etal-2020-label
%X Several recent state-of-the-art transfer learning methods model classification tasks as text generation, where labels are represented as strings for the model to generate. We investigate the effect that the choice of strings used to represent labels has on how effectively the model learns the task. For four standard text classification tasks, we design a diverse set of possible string representations for labels, ranging from canonical label definitions to random strings. We experiment with T5 on these tasks, varying the label representations as well as the amount of training data. We find that, in the low data setting, label representation impacts task performance on some tasks, with task-related labels being most effective, but fails to have an impact on others. In the full data setting, our results are largely negative: Different label representations do not affect overall task performance.
%R 10.18653/v1/2020.aacl-srw.23
%U https://aclanthology.org/2020.aacl-srw.23
%U https://doi.org/10.18653/v1/2020.aacl-srw.23
%P 160-164
Markdown (Informal)
[Label Representations in Modeling Classification as Text Generation](https://aclanthology.org/2020.aacl-srw.23) (Chen et al., AACL 2020)
ACL
- Xinyi Chen, Jingxian Xu, and Alex Wang. 2020. Label Representations in Modeling Classification as Text Generation. In Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing: Student Research Workshop, pages 160–164, Suzhou, China. Association for Computational Linguistics.