  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.
