论文标题
SMRT聊天机器人：通过模拟多个参考培训改进非任务的对话框
SMRT Chatbots: Improving Non-Task-Oriented Dialog with Simulated Multiple Reference Training
论文作者
论文摘要
非任务为导向的对话模型的质量差和非多样性响应都遭受。为了克服有限的对话数据，我们应用了模拟的多个参考培训（SMRT； Khayrallah等，2020），并使用释义器来模拟每个训练提示的多个响应。我们发现，通过人类和自动质量得分和词汇多样性衡量的强大变压器基线的SMRT改善。我们还发现，SMRT与人类评估质量的预处理相媲美，并且在不需要相关域的对话数据的情况下，对自动质量和词汇多样性进行了胜过。
Non-task-oriented dialog models suffer from poor quality and non-diverse responses. To overcome limited conversational data, we apply Simulated Multiple Reference Training (SMRT; Khayrallah et al., 2020), and use a paraphraser to simulate multiple responses per training prompt. We find SMRT improves over a strong Transformer baseline as measured by human and automatic quality scores and lexical diversity. We also find SMRT is comparable to pretraining in human evaluation quality, and outperforms pretraining on automatic quality and lexical diversity, without requiring related-domain dialog data.
