2020
EMNLP
EMNLP 2020
Label-Efficient Training for Next Response Selection
Abstract
AbstractThis 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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Interdisciplinary Bridge
- Artificial Intelligence and Natural Language Processing
Authors
Topics
Machine Learning > Learning Types > Self-Supervised Learning
Machine Learning > Learning Types > Semi-Supervised Learning
Machine Learning > Learning Types > Weakly Supervised Learning
Natural Language Processing > Generation > Dialogue Systems
Deep Learning > Learning Types > Self-Supervised Learning