%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