Topical-Chat：迈向基于知识的开放域对话
Topical-Chat: Towards Knowledge-Grounded Open-Domain Conversations
AI总结：
针对现有知识型对话数据集程式化、话题覆盖不足的问题，研究者推出含8类话题、无明确角色的Topical-Chat人-人对话数据集，并训练基准模型开展评估，助力开放域对话AI研究。
摘要：
构建能够与人类进行深入、有吸引力的开放域对话的社交机器人是人工智能（AI）的重大挑战之一。为此，当与拥有自身世界知识的人类对话时，机器人需要能够有效利用跨多个领域的世界知识。现有的基于知识的对话数据集主要是程式化的，对话伙伴有明确的角色。这些数据集也未探究对话中话题转换的话题覆盖深度与广度。我们推出Topical-Chat，这是一个基于知识的人-人对话数据集，其底层知识涵盖8个广泛话题，且对话伙伴没有明确定义的角色，以助力开放域对话AI的进一步研究。我们还在Topical-Chat上训练了多个最先进的编码器-解码器对话模型，并进行了自动评估与人工评估以建立基准。
英文摘要：
Building socialbots that can have deep, engaging open-domain conversations with humans is one of the grand challenges of artificial intelligence (AI). To this end, bots need to be able to leverage world knowledge spanning several domains effectively when conversing with humans who have their own world knowledge. Existing knowledge-grounded conversation datasets are primarily stylized with explicit roles for conversation partners. These datasets also do not explore depth or breadth of topical coverage with transitions in conversations. We introduce Topical-Chat, a knowledge-grounded human-human conversation dataset where the underlying knowledge spans 8 broad topics and conversation partners don't have explicitly defined roles, to help further research in open-domain conversational AI. We also train several state-of-the-art encoder-decoder conversational models on Topical-Chat and perform automated and human evaluation for benchmarking.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
