mlsanityData to 7 Oct 2026

Counterfactual Data Augmentation via Perspective Transition for Open-Domain Dialogues

arXiv 2210.16838 ↗First posted 30 Oct 2022cs.CL cs.AIcited by 6

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Abstract (arXiv)

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.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 35 of its 58 references that are in the map. First posted on arXiv 30 Oct 2022.

Builds on

35 of 58 references.

References · 35

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23 more
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Built on by

Cited by year

Citing papers per year: 2022: 1; 2023: 3; 2024: 2.

6 papers.

  1. 2022SODA: Million-scale Dialogue Distillation with Social Commonsense Contextualizationcited by 166
  2. 2023Prompting Large Language Models for Counterfactual Generation: An Empirical Studycited by 36
  3. 2023Learning by Analogy: Diverse Questions Generation in Math Word Problemcited by 18
  4. 2023Improving Conversational Recommendation Systems via Counterfactual Data Simulationcited by 10
  5. 2024Inductive-Deductive Strategy Reuse for Multi-Turn Instructional Dialoguescited by 6
  6. 2024Controllable and Diverse Data Augmentation with Large Language Model for Low-Resource Open-Domain Dialogue Generationcited by 4