Interaction Matters: An Evaluation Framework for Interactive Dialogue Assessment on English Second Language Conversations
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arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| _version_ | 1866910812817326080 |
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| author | Gao, Rena Roever, Carsten Lau, Jey Han |
| author_facet | Gao, Rena Roever, Carsten Lau, Jey Han |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_06479 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Interaction Matters: An Evaluation Framework for Interactive Dialogue Assessment on English Second Language Conversations Gao, Rena Roever, Carsten Lau, Jey Han Computation and Language Social and Information Networks 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. |
| title | Interaction Matters: An Evaluation Framework for Interactive Dialogue Assessment on English Second Language Conversations |
| topic | Computation and Language Social and Information Networks |
| url | https://arxiv.org/abs/2407.06479 |