@inproceedings{choi-etal-2020-label,
title = "Label-Efficient Training for Next Response Selection",
author = "Choi, Seungtaek and
Jeong, Myeongho and
Yeo, Jinyoung and
Hwang, Seung-won",
editor = "Moosavi, Nafise Sadat and
Fan, Angela and
Shwartz, Vered and
Glava{\v{s}}, Goran and
Joty, Shafiq and
Wang, Alex and
Wolf, Thomas",
booktitle = "Proceedings of SustaiNLP: Workshop on Simple and Efficient Natural Language Processing",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.sustainlp-1.22",
doi = "10.18653/v1/2020.sustainlp-1.22",
pages = "164--168",
abstract = "This paper studies label augmentation for training dialogue response selection. The existing model is trained by {``}observational{''} annotation, where one observed response is annotated as gold. In this paper, we propose {``}counterfactual augmentation{''} of pseudo-positive labels. We validate that the effectiveness of augmented labels are comparable to positives, such that ours outperform state-of-the-arts without augmentation.",
}
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<abstract>This paper studies label augmentation for training dialogue response selection. The existing model is trained by “observational” annotation, where one observed response is annotated as gold. In this paper, we propose “counterfactual augmentation” of pseudo-positive labels. We validate that the effectiveness of augmented labels are comparable to positives, such that ours outperform state-of-the-arts without augmentation.</abstract>
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%0 Conference Proceedings
%T Label-Efficient Training for Next Response Selection
%A Choi, Seungtaek
%A Jeong, Myeongho
%A Yeo, Jinyoung
%A Hwang, Seung-won
%Y Moosavi, Nafise Sadat
%Y Fan, Angela
%Y Shwartz, Vered
%Y Glavaš, Goran
%Y Joty, Shafiq
%Y Wang, Alex
%Y Wolf, Thomas
%S Proceedings of SustaiNLP: Workshop on Simple and Efficient Natural Language Processing
%D 2020
%8 November
%I Association for Computational Linguistics
%C Online
%F choi-etal-2020-label
%X This paper studies label augmentation for training dialogue response selection. The existing model is trained by “observational” annotation, where one observed response is annotated as gold. In this paper, we propose “counterfactual augmentation” of pseudo-positive labels. We validate that the effectiveness of augmented labels are comparable to positives, such that ours outperform state-of-the-arts without augmentation.
%R 10.18653/v1/2020.sustainlp-1.22
%U https://aclanthology.org/2020.sustainlp-1.22
%U https://doi.org/10.18653/v1/2020.sustainlp-1.22
%P 164-168
Markdown (Informal)
[Label-Efficient Training for Next Response Selection](https://aclanthology.org/2020.sustainlp-1.22) (Choi et al., sustainlp 2020)
ACL
- Seungtaek Choi, Myeongho Jeong, Jinyoung Yeo, and Seung-won Hwang. 2020. Label-Efficient Training for Next Response Selection. In Proceedings of SustaiNLP: Workshop on Simple and Efficient Natural Language Processing, pages 164–168, Online. Association for Computational Linguistics.