Chat Detection in an Intelligent Assistant: Combining Task-oriented and Non-task-oriented Spoken Dialogue Systems
Chat Detection in an Intelligent Assistant: Combining Task-oriented and Non-task-oriented Spoken Dialogue Systems
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DOI：
10.18653/v1/p17-1120
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发表时间：
2017-05
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影响因子：
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通讯作者：
Satoshi Akasaki;Nobuhiro Kaji
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文献类型：
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作者：
Satoshi Akasaki;Nobuhiro Kaji
智能手机和家用电子产品上近期出现的智能助手（例如Siri和Alexa）可以被视为特定领域面向任务的口语对话系统和开放领域非面向任务的系统的新型混合体。为了实现这种混合对话系统，本文研究确定用户是否要与系统聊天。为了解决该任务缺乏基准数据集的问题，我们构建了一个新的数据集，其中包含从一个商业智能助手的真实日志数据中收集的15160条语句（并且将会发布该数据集以促进未来的研究活动）。此外，我们研究使用推文和网络搜索查询来处理开放领域的用户语句，这是聊天检测任务的特点。实验表明，虽然简单的监督方法是有效的，但使用推文和搜索查询将F1分数从86.21进一步提高到了87.53。
Recently emerged intelligent assistants on smartphones and home electronics (e.g., Siri and Alexa) can be seen as novel hybrids of domain-specific task-oriented spoken dialogue systems and open-domain non-task-oriented ones. To realize such hybrid dialogue systems, this paper investigates determining whether or not a user is going to have a chat with the system. To address the lack of benchmark datasets for this task, we construct a new dataset consisting of 15,160 utterances collected from the real log data of a commercial intelligent assistant (and will release the dataset to facilitate future research activity). In addition, we investigate using tweets and Web search queries for handling open-domain user utterances, which characterize the task of chat detection. Experimental experiments demonstrated that, while simple supervised methods are effective, the use of the tweets and search queries further improves the F_1-score from 86.21 to 87.53.