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Multi-source inverse-curriculum-based training for low-resource dialogue generation

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Abstract

An effective dialogue system needs amount of training data, but the existing training data is insufficient. Although the pre-trained model has made great progress in recent years, which can alleviate the problem of low resource dialogue to a certain extent, the pre-trained model is large and difficult to deploy. How to improve the performance of dialogue model without additional annotation data and decreasing the model volume has become a new challenge. We propose a multi-source data augmentation method for low-resource dialogue generation by utilizing inverse curriculum learning (inverse CL). Firstly, we adopt three data augmentation methods, including round-trip translation, paraphrasing and pre-trained model, to generate augmentation data. Next, we propose a new training strategy based on inverse CL to utilize different augmentation data. Comparing with the baselines, our method comprehensively outperform the baselines on all evaluation metrics, which shows the effectiveness of our proposed training strategy for dialogue generation. To the best of our knowledge, this is the first systematic investigation of data augmentation in the dialogue generation.

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Notes

  1. https://github.com/marian-nmt/marian

  2. https://github.com/vsuthichai/paraphraser

  3. https://github.com/LHolten/DialoGPT-MMI-decoder

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Acknowledgements

The research work descried in this paper has been supported by the National Key R&D Program of China (2019YFB1405200) and the National Natural Science Foundation of China (No. 61976015, 61976016, 61876198 and 61370130). The authors would like to thank the anonymous reviewers for their valuable comments and suggestions to improve this paper.

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Authors and Affiliations

  1. Institute of Advanced Control System, School of Electronic Information Engineering, Beijing Jiaotong University, No.3 Shangyuan Road, Haidian, Beijing, 100044, People’s Republic of China

    Fuwei Cui & Ze Liu

  2. Toshiba (China) Co., Ltd, No.19 Dongfang East Road, Chaoyang, Beijing, 100600, People’s Republic of China

    Hui Di & Kazushige Ouchi

  3. School of Computer Information Technology, Beijing Jiaotong University, No.3 Shangyuan Road, Haidian, Beijing, 100044, People’s Republic of China

    Hui Huang, Hongjie Ren & Jinan Xu

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  1. Fuwei Cui
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Correspondence to Jinan Xu.

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Cui, F., Di, H., Huang, H. et al. Multi-source inverse-curriculum-based training for low-resource dialogue generation. Appl Intell 53, 13665–13676 (2023). https://doi.org/10.1007/s10489-022-04190-z

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