We present an evaluation framework for interactive dialogue assessment in the context of English as a Second Language (ESL) speakers. Our framework collects dialogue-level interactivity labels (e.g., topic management; 4 labels in total) and micro-level span features (e.g., backchannels; 17 features in total). Given our annotated data, we study how the micro-level features influence the (higher level) interactivity quality of ESL dialogues by constructing various machine learning-based models. Our results demonstrate that certain micro-level features strongly correlate with interactivity quality, like reference word (e.g., she, her, he), revealing new insights about the interaction between higher-level dialogue quality and lower-level linguistic signals. Our framework also provides a means to assess ESL communication, which is useful for language assessment.

翻译：本文提出了一种针对英语作为第二语言（ESL）使用者的互动对话评估框架。该框架收集对话层面的互动性标签（例如话题管理；共4类标签）以及微观层面的片段特征（例如反馈性提示；共17项特征）。基于标注数据，我们通过构建多种机器学习模型，研究了微观特征如何影响ESL对话的（更高层面）互动质量。实验结果表明，部分微观特征（如指称词“she”“her”“he”等）与互动质量存在强相关性，这揭示了高层对话质量与底层语言信号之间的交互关系。本框架同时为ESL交际能力评估提供了有效工具，对语言测评具有实用价值。