A dialogue replacement data augmentation method based on comparing belief state and dialogue acts

Runjun Chen; Zheng Ye; Jun Qin; Jing Liu

doi:10.1117/12.2656495

20 October 2022 A dialogue replacement data augmentation method based on comparing belief state and dialogue acts

Runjun Chen, Zheng Ye, Jun Qin, Jing Liu

Proceedings Volume 12451, 5th International Conference on Computer Information Science and Application Technology (CISAT 2022); 124512W (2022) https://doi.org/10.1117/12.2656495
Event: 5th International Conference on Computer Information Science and Application Technology (CISAT 2022), 2022, Chongqing, China

Abstract

Training a dialogue generation model requires large-scale, high-quality dialogue data. Data augmentation is a common method to expand a dataset. Some common data augmentation methods such as back-translation and synonym replacement are used to augment dialogue datasets, but these methods are only aimed at word or sentence level changes and do not consider the relationship between dialogues to generate new dialogue data and the meaning of the generated sentences may conflict with the context of the dialogue. To solve this problem, a new data augmentation method is proposed for expanding the task-oriented dialogues. Solve the problem of insufficient training data by looking for a dialogue in the original dataset, then replacing the dialogue after comparing the belief state and dialogue acts. Experimental results on MultiWOZ dataset show that this method can improve the Combined Score by 0.99, 0.24 and 1.5 respectively compared with the data augmentation method at the character-level, word-level and the sentence-level.

Citation Download Citation

Runjun Chen, Zheng Ye, Jun Qin, and Jing Liu "A dialogue replacement data augmentation method based on comparing belief state and dialogue acts", Proc. SPIE 12451, 5th International Conference on Computer Information Science and Application Technology (CISAT 2022), 124512W (20 October 2022); https://doi.org/10.1117/12.2656495

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