Generative open-domain dialogue systems can benefit from external knowledge, but the lack of external knowledge resources and the difficulty in finding relevant knowledge limit the development of this technology. To this end, we propose a knowledge-driven dialogue task using dynamic service information. Specifically, we use a large number of service APIs that can provide high coverage and spatiotemporal sensitivity as external knowledge sources. The dialogue system generates queries to request external services along with user information, get the relevant knowledge, and generate responses based on this knowledge. To implement this method, we collect and release the first open domain Chinese service knowledge dialogue dataset DuSinc. At the same time, we construct a baseline model PLATO-SINC, which realizes the automatic utilization of service information for dialogue. Both automatic evaluation and human evaluation show that our proposed new method can significantly improve the effect of open-domain conversation, and the session-level overall score in human evaluation is improved by 59.29% compared with the dialogue pre-training model PLATO-2. The dataset and benchmark model will be open sourced.
翻译:外部知识可以带来开源对话系统,但缺乏外部知识资源以及难以找到相关知识,限制了这一技术的发展。为此,我们提出使用动态服务信息进行知识驱动的对话任务。具体地说,我们使用大量服务性API作为外部知识来源,可以提供高覆盖率和时空敏感度。对话系统产生查询,要求外部服务以及用户信息,获得相关知识,并根据这种知识作出响应。为了实施这一方法,我们收集并发布第一个开放域中国服务知识对话数据集DuSinc。与此同时,我们建立了一个基线模型PLATO-SINC,实现自动利用服务信息进行对话。自动评价和人类评价都表明,我们提议的新方法可以大大改善开放式对话的效果,会议一级的人的评价总分比对话前模式PLATO-2提高了59.29 %。数据元和基准模型将开源。