Despite growing interest in applications based on natural customer support
conversations, there exist remarkably few publicly available datasets that
reflect the expected characteristics of conversations in these settings.
Existing task-oriented dialogue datasets, which were collected to benchmark
dialogue systems mainly in written human-to-bot settings, are not
representative of real customer support conversations and do not provide
realistic benchmarks for systems that are applied to natural data. To address
this gap, we introduce NatCS, a multi-domain collection of spoken customer
service conversations. We describe our process for collecting synthetic
conversations between customers and agents based on natural language phenomena
observed in real conversations. Compared to previous dialogue datasets, the
conversations collected with our approach are more representative of real
human-to-human conversations along multiple metrics. Finally, we demonstrate
potential uses of NatCS, including dialogue act classification and intent
induction from conversations as potential applications, showing that dialogue
act annotations in NatCS provide more effective training data for modeling real
conversations compared to existing synthetic written datasets. We publicly
release NatCS to facilitate research in natural dialog systems

介绍了一种多领域语音客户服务对话的数据集，名为 NatCS，用于研究自然语言对话系统，相对于现有的合成对话数据集，在多个指标上更加真实，其中对话行为的注释提供了更有效的训练数据。