Different people have different habits of describing their intents in conversations. Some people tend to deliberate their intents in several successive utterances, i.e., they use several consistent messages for readability instead of a long sentence to express their question. This creates a predicament faced by the application of dialogue systems, especially in real-world industry scenarios, in which the dialogue system is unsure whether it should answer the query of user immediately or wait for further supplementary input. Motivated by such an interesting predicament, we define a novel Wait-or-Answer task for dialogue systems. We shed light on a new research topic about how the dialogue system can be more intelligent to behave in this Wait-or-Answer quandary. Further, we propose a predictive approach named Predict-then-Decide (PTD) to tackle this Wait-or-Answer task. More specifically, we take advantage of a decision model to help the dialogue system decide whether to wait or answer. The decision of decision model is made with the assistance of two ancillary prediction models: a user prediction and an agent prediction. The user prediction model tries to predict what the user would supplement and uses its prediction to persuade the decision model that the user has some information to add, so the dialogue system should wait. The agent prediction model tries to predict the answer of the dialogue system and convince the decision model that it is a superior choice to answer the query of user immediately since the input of user has come to an end. We conduct our experiments on two real-life scenarios and three public datasets. Experimental results on five datasets show our proposed PTD approach significantly outperforms the existing models in solving this Wait-or-Answer problem.
翻译:不同的人们有不同的习惯来描述他们在对话中的意图。 有些人倾向于在一系列连续的演讲中思考他们的意图, 也就是说, 他们使用几个一致的信息来表达他们的问题, 而不是长长的句子来表达他们的问题。 这在应用对话系统时造成了一种困境, 特别是在现实世界的行业情景中, 对话系统无法确定它应该立即回答用户的询问还是等待进一步的补充输入。 受这种有趣的困境的驱使, 我们定义了一个新的对话系统“ 等待或答复” 任务。 我们给出了一个新的研究课题, 即对话系统如何在这个“ 等待” 或“ Answer ” 的二次演讲中更明智地采取行动。 此外, 我们提出了一种预测性的方法, 叫做“ 预测性- 执行者- Decide (PTD) (PTD) (PTD) (PTD) (PTD) (PTD) (Pid- ) ) 来应对这一任务。 。 更具体地说, 我们利用一个决定性模型来帮助对话系统决定是等待还是回答。 我们的决定模式在两种辅助的预测性方法: 用户预测和代理人预测性 预测性解释。 一些用户的模型会试图在判断性对话中, 之后, 显示用户的判断性决定的答案。