Building a natural language dataset requires caution since word semantics is
vulnerable to subtle text change or the definition of the annotated concept.
Such a tendency can be seen in generative tasks like question-answering and
dialogue generation and also in tasks that create a categorization-based
corpus, like topic classification or sentiment analysis. Open-domain
conversations involve two or more crowdworkers freely conversing about any
topic, and collecting such data is particularly difficult for two reasons: 1)
the dataset should be ``crafted" rather than ``obtained" due to privacy
concerns, and 2) paid creation of such dialogues may differ from how
crowdworkers behave in real-world settings. In this study, we tackle these
issues when creating a large-scale open-domain persona dialogue corpus, where
persona implies that the conversation is performed by several actors with a
fixed persona and user-side workers from an unspecified crowd.

本研究创建了一个大规模的开放式人设对话语料库，解决了创建自然语言数据集时面对的语义敏感性、隐私问题、数据采集难度等多重难题。