Addressee detection is a fundamental task for seamless dialogue management and turn taking in human-agent interaction. Though addressee detection is implicit in dyadic interaction, it becomes a challenging task when more than two participants are involved. This article proposes multiple addressee detection models based on smart feature selection and focus encoding schemes. The models are trained using different machine learning and deep learning algorithms. This research work improves existing baseline accuracies for addressee prediction on two datasets. In addition, the article explores the impact of different focus encoding schemes in several addressee detection cases. Finally, an implementation strategy for addressee detection model in real-time is discussed.

收件人检测是无缝对话管理和接手人与人之间的交互的一项基本任务。尽管收件人的检测在二元交互中是隐式的，但是当涉及两个以上的参与者时，这成为一项具有挑战性的任务。本文提出了基于智能特征选择和焦点编码方案的多个收件人检测模型。使用不同的机器学习和深度学习算法训练模型。这项研究工作改善了在两个数据集上进行收件人预测的现有基准精度。此外，本文探讨了在几种收件人检测情况下不同焦点编码方案的影响。最后，讨论了一种实时的收件人地址检测模型的实现策略。