论文标题
探索重复，记忆和基于注意力的体系结构，以评分人类机器文本对话框的交互方面
Exploring Recurrent, Memory and Attention Based Architectures for Scoring Interactional Aspects of Human-Machine Text Dialog
论文作者
论文摘要
使英语学习者提高他们的会话性演讲能力的重要一步是对互动能力的多个方面的自动评分以及随后的目标反馈。本文基于此方向以前的工作来研究多个神经体系结构（基于重复，注意力和内存），以及针对文本对话框数据的互动和主题开发方面自动评分的功能工程模型。我们在人类学习者的文本对话框的对话数据库上进行了实验，这些对话者与基于云的对话系统进行交互，该系统沿着对话能力的多个维度进行了三个评分。我们发现，多个体系结构的融合在我们的自动评分任务相对于专家间协议的自动评分任务上有效，并且（i）手工设计的功能传递给了支持向量学习者，并且（ii）基于变压器的架构最为突出。
An important step towards enabling English language learners to improve their conversational speaking proficiency involves automated scoring of multiple aspects of interactional competence and subsequent targeted feedback. This paper builds on previous work in this direction to investigate multiple neural architectures -- recurrent, attention and memory based -- along with feature-engineered models for the automated scoring of interactional and topic development aspects of text dialog data. We conducted experiments on a conversational database of text dialogs from human learners interacting with a cloud-based dialog system, which were triple-scored along multiple dimensions of conversational proficiency. We find that fusion of multiple architectures performs competently on our automated scoring task relative to expert inter-rater agreements, with (i) hand-engineered features passed to a support vector learner and (ii) transformer-based architectures contributing most prominently to the fusion.
