Counterfactual Data Augmentation via Perspective Transition for Open-Domain Dialogues (2022)

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.

## introduction open-domain dialogue systems have attracted much attention @xcite @xcite due to their potential applications. generally, training opendomain dialogue systems requires high-quality dialogue datasets. the dialogue data admits a wide variety of responses for a given dialogue history @xcite . specifically, a given dialogue history can exist many valid responses with different semantics, and the response of each semantic information can also have abundant alternative expressions @xcite . however, manually collecting high-quality such datasets is usually laborintensive and time-consuming in practice. a feasible solution to address this problem is to use data augmentation techniques. currently, smoking is harmful for your health. ## background in this section, we describe task definitions and review the concept of the structural causal model. ## method in this section, our goal is to take an input dialogue sample (x, y) and augment high-quality responses ## conclusion this paper presents a counterfactual data augmentation method, capt, to augment more responses with different semantics for a given dialogue history. specifically, capt employs counterfactual inference to generate counterfactual responses by intervening in the observed reply perspective, which replaces with different reply perspectives for generating semantically different responses. experimental results show that capt can augment highquality responses with different semantics, which can be further used to improve the performance of downstream tasks. in future work, we plan to explore an appropriate training strategy for further preventing dialogue models from being affected by noises in our augmented data, and extend capt on multi-turn dialogues. we hope that capt will encourage future research for other generation tasks.