EVA2.0: Investigating Open-Domain Chinese Dialogue Systems with Large-Scale Pre-Training

• URL: http://arxiv.org/abs/2203.09313v3
• Date: Sat, 21 Oct 2023 15:36:48 GMT
• Title: EVA2.0: Investigating Open-Domain Chinese Dialogue Systems with Large-Scale Pre-Training
• Authors: Yuxian Gu, Jiaxin Wen, Hao Sun, Yi Song, Pei Ke, Chujie Zheng, Zheng Zhang, Jianzhu Yao, Lei Liu, Xiaoyan Zhu, Minlie Huang
• Abstract summary: EVA2.0 is a large-scale pre-trained open-domain Chinese dialogue model with 2.8 billion parameters. 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.
• Score: 73.98154158068134
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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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