Interaction Matters: An Evaluation Framework for Interactive Dialogue Assessment on English Second Language Conversations
<|reference_start|>Interaction Matters: An Evaluation Framework for Interactive Dialogue Assessment on English Second Language Conversations: 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.<|reference_end|>
@article{gao2024interaction,
    title={Interaction Matters: An Evaluation Framework for Interactive Dialogue
  Assessment on English Second Language Conversations},
    author={Rena Gao, Carsten Roever, Jey Han Lau},
    journal={arXiv preprint arXiv:2407.06479},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.06479},
    primaryClass={cs.CL cs.SI}
}