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arXiv · 2407.06479

Interaction Matters: An Evaluation Framework for Interactive Dialogue Assessment on English Second Language Conversations

Rena Gao·Carsten Roever·Jey Han Lau
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Abstract

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.

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Rena Gao, Carsten Roever, Jey Han Lau. 2024-07-09. Interaction Matters: An Evaluation Framework for Interactive Dialogue Assessment on English Second Language Conversations. https://arxiv.org/abs/2407.06479

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