论文标题
一个混合解决方案，以学习基于多方服务的聊天组中的转弯
A Hybrid Solution to Learn Turn-Taking in Multi-Party Service-based Chat Groups
论文作者
论文摘要
预测在多方对话中进行互动的下一个最有可能的参与者是一个困难的问题。在基于文本的聊天组中，唯一可用的信息是发件人，文本的内容和对话历史记录。在本文中，我们介绍了如何通过基于最大似然期望（MLE），卷积神经网络（CNN）和有限状态自动机（FSA）整合转弯分类器的语料库和架构来将这些信息用于预测任务的研究。该语料库是多域向导数据集（Multiwoz）的合成改编，以带有对话错误的多个基于旅行服务的机器人方案，并创建以模拟用户的交互并评估体系结构。我们提出了实验结果，该结果表明，CNN方法的性能要比基线，精度为92.34％，但是使用MLE，CNN和FSA的集成解决方案的性能甚至更好，以95.65％的速度实现了更好的性能。
To predict the next most likely participant to interact in a multi-party conversation is a difficult problem. In a text-based chat group, the only information available is the sender, the content of the text and the dialogue history. In this paper we present our study on how these information can be used on the prediction task through a corpus and architecture that integrates turn-taking classifiers based on Maximum Likelihood Expectation (MLE), Convolutional Neural Networks (CNN) and Finite State Automata (FSA). The corpus is a synthetic adaptation of the Multi-Domain Wizard-of-Oz dataset (MultiWOZ) to a multiple travel service-based bots scenario with dialogue errors and was created to simulate user's interaction and evaluate the architecture. We present experimental results which show that the CNN approach achieves better performance than the baseline with an accuracy of 92.34%, but the integrated solution with MLE, CNN and FSA achieves performance even better, with 95.65%.
