NatCS：获取自然客户支持对话
NatCS: Eliciting Natural Customer Support Dialogues
AI总结：
针对现有任务型对话数据集无法代表真实客户支持对话的问题，该研究推出多领域口语客户服务对话数据集NatCS，其合成对话更贴近真实人际对话，可提升对话行为分类等任务的训练效果，已公开发布以助力自然对话系统研究。
摘要：
尽管人们对基于自然客户支持对话的应用兴趣日益浓厚，但目前公开可用的、能反映这类场景下对话预期特征的数据集却极为稀少。现有的任务型对话数据集主要是在书面人机交互场景下收集、用于为对话系统提供基准测试的，无法代表真实的客户支持对话，也不能为应用于自然数据的系统提供符合实际的基准。为填补这一空白，我们推出NatCS——一个多领域的口语客户服务对话集合。我们介绍了基于真实对话中观察到的自然语言现象，收集客户与客服之间合成对话的流程。与过往的对话数据集相比，通过我们的方法收集的对话在多项指标上更能代表真实的人与人之间的对话。最后，我们展示了NatCS的潜在用途，包括对话行为分类和对话意图归纳等潜在应用，表明与现有的合成书面数据集相比，NatCS中的对话行为标注能为真实对话建模提供更有效的训练数据。我们公开发布NatCS，以推动自然对话系统领域的研究。
英文摘要：
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
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。
