SAD: A Stress Annotated Dataset for Recognizing Everyday Stressors in SMS-like Conversational Systems
SAD: A Stress Annotated Dataset for Recognizing Everyday Stressors in SMS-like Conversational Systems
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SAD：用于识别类似短信对话系统中日常压力源的压力注释数据集
DOI：
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发表时间：
2021
期刊：
影响因子：
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通讯作者：
P. Paredes
中科院分区：
文献类型：
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作者：
M. Mauriello;Emmanuel Thierry Lincoln;Grace Hon;Dorien Simon;Dan Jurafsky;P. Paredes
向有需要的人提供压力管理服务的基础设施有限。为了解决这个问题，聊天机器人被视为一种可扩展的解决方案。然而，一个限制因素是对日常压力有明确的定义和例子，在此基础上建立模型和方法，以便在对话中传递适当的建议。我们开发了一个包含6850个类似短信的句子的数据集，可以使用9个压力源类别的方案对输入进行分类，这些压力源类别来自：压力管理文献、来自原型聊天机器人系统的现场对话、众包和从在线资源库中有针对性的网络抓取。除了发布这个数据集之外，我们还展示了在分类方面很有希望的结果。我们的贡献包括：(I)日常应激源的分类，(Ii)短信类句子的数据集，(Iii)对该数据集的分析，展示其潜在的有效性，以及(Iv)通过模拟模型响应时间来演示其实施的有效性。
There is limited infrastructure for providing stress management services to those in need. To address this problem, chatbots are viewed as a scalable solution. However, one limiting factor is having clear definitions and examples of daily stress on which to build models and methods for routing appropriate advice during conversations. We developed a dataset of 6850 SMS-like sentences that can be used to classify input using a scheme of 9 stressor categories derived from: stress management literature, live conversations from a prototype chatbot system, crowdsourcing, and targeted web scraping from an online repository. In addition to releasing this dataset, we show results that are promising for classification purposes. Our contributions include: (i) a categorization of daily stressors, (ii) a dataset of SMS-like sentences, (iii) an analysis of this dataset that demonstrates its potential efficacy, and (iv) a demonstration of its utility for implementation via a simulation of model response times.