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：我们提出了一个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个标签）以及微观层面的片段特征（例如反馈信号；共17个特征）。基于已标注数据，我们通过构建多种基于机器学习的模型，研究了微观层面特征如何影响ESL对话的（更高层面）互动性质量。我们的结果表明，某些微观特征（如指称词（例如she、her、he））与互动性质量存在强相关性，这揭示了高层对话质量与低层语言信号之间相互作用的新见解。本框架也为评估ESL交流能力提供了工具，对语言评估具有实用价值。