Yuxian Gu, Jiaxin Wen, Hao Sun, Yi Song, Pei Ke, Chujie Zheng, Zheng Zhang, Jianzhu Yao, Lei Liu, Xiaoyan Zhu, Minlie Huang. EVA2.0: Investigating Open-domain Chinese Dialogue Systems with Large-scale Pre-training[J]. Machine Intelligence Research, 2023, 20(2): 207-219. DOI: 10.1007/s11633-022-1387-3
Citation: Yuxian Gu, Jiaxin Wen, Hao Sun, Yi Song, Pei Ke, Chujie Zheng, Zheng Zhang, Jianzhu Yao, Lei Liu, Xiaoyan Zhu, Minlie Huang. EVA2.0: Investigating Open-domain Chinese Dialogue Systems with Large-scale Pre-training[J]. Machine Intelligence Research, 2023, 20(2): 207-219. DOI: 10.1007/s11633-022-1387-3

EVA2.0: Investigating Open-domain Chinese Dialogue Systems with Large-scale Pre-training

  • Large-scale pre-training has shown remarkable performance in building open-domain dialogue systems. However, previous works mainly focus on showing and evaluating the conversational performance of the released dialogue model, ignoring the discussion of some key factors towards a powerful human-like chatbot, especially in Chinese scenarios. In this paper, we conduct extensive experiments to investigate these under-explored factors, including data quality control, model architecture designs, training approaches, and decoding strategies. We propose EVA2.0, a large-scale pre-trained open-domain Chinese dialogue model with 2.8 billion parameters, and will make our models and codes publicly available. Automatic and human evaluations show that EVA2.0 significantly outperforms other open-source counterparts. We also discuss the limitations of this work by presenting some failure cases and pose some future research directions on large-scale Chinese open-domain dialogue systems.
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