Counterfactual Data Augmentation via Perspective Transition for Open-Domain Dialogues

TL;DR

This paper introduces a counterfactual data augmentation approach for open-domain dialogues that generates semantically diverse responses to improve dialogue system training, outperforming baselines.

Contribution

It proposes a novel counterfactual inference method to automatically augment dialogue datasets with diverse responses, reducing manual data collection efforts.

Findings

Augmented datasets improve downstream dialogue task performance.

The method generates semantically varied responses effectively.

Outperforms existing baselines in experiments.

Abstract

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.