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. Experiments demonstrated that, while simple supervised
methods are effective, the use of the tweets and search queries further
improves the F1-score from 86.21 to 87.53.

研究智能助手是否可以匹配用户的聊天意图，构建了新的数据集以及使用推特和 Web 搜索查询来处理无特定领域的用户发言，实验结果表明，使用 tweets 和 Web 搜索查询可以进一步提高智能助手的 F1 分数。