Counterfactual Data Augmentation via Perspective Transition for Open-Domain Dialogues
The construction of open-domain dialogue systems requires high-quality dialogue datasets. The dialogue data admits a wide variety of responses for a given dialogue history, especially responses with different semantics. However, collecting high-quality such a dataset in most scenarios is labor-intensive and time-consuming. In this paper, we propose a data augmentation method to automatically augment high-quality responses with different semantics by counterfactual inference. Specifically, given an observed dialogue, our counterfactual generation model first infers semantically different responses by replacing the observed reply perspective with substituted ones. Furthermore, our data selection method filters out detrimental augmented responses. Experimental results show that our data augmentation method can augment high-quality responses with different semantics for a given dialogue history, and can outperform competitive baselines on multiple downstream tasks.
Code (1)
Tasks
counterfactualCounterfactual InferenceData AugmentationSimilar Papers 제목 키워드 기반
MoCoDA: Model-based Counterfactual Data Augmentation
The number of states in a dynamic process is exponential in the number of objects, making reinforcement learning (RL) difficult in complex, multi-object domains. For agents to scale to the real world, they will need to r…
counterfactualData AugmentationmodelOffline RL+1Implicit Counterfactual Data Augmentation for Robust Learning
Machine learning models are prone to capturing the spurious correlations between non-causal attributes and classes, with counterfactual data augmentation being a promising direction for breaking these spurious associatio…
counterfactualData AugmentationMeta-LearningOut-of-Distribution GeneralizationRetrieval-guided Counterfactual Generation for QA
Deep NLP models have been shown to be brittle to input perturbations. Recent work has shown that data augmentation using counterfactuals --- i.e. minimally perturbed inputs --- can help ameliorate this weakness. We focus…
counterfactualData AugmentationDiversityQuestion Answering+5Retrieval-guided Counterfactual Generation for QA
Deep NLP models have been shown to learn spurious correlations, leaving them brittle to input perturbations. Recent work has shown that counterfactual or contrastive data -- i.e. minimally perturbed inputs -- can reveal …
counterfactualData AugmentationDiversityQuestion Answering+5Counterfactual experience augmented off-policy reinforcement learning
Reinforcement learning control algorithms face significant challenges due to out-of-distribution and inefficient exploration problems. While model-based reinforcement learning enhances the agent's reasoning and planning …
counterfactualCounterfactual InferenceDensity EstimationModel-based Reinforcement Learning+2