EDEN: Empathetic Dialogues for English Learning

Siyan Li, Teresa Shao, Zhou Yu, Julia Hirschberg


Abstract
Dialogue systems have been used as conversation partners in English learning, but few have studied whether these systems improve learning outcomes. Student passion and perseverance, or grit, has been associated with language learning success. Recent work establishes that as students perceive their English teachers to be more supportive, their grit improves. Hypothesizing that the same pattern applies to English-teaching chatbots, we create EDEN, a robust open-domain chatbot for spoken conversation practice that provides empathetic feedback. To construct EDEN, we first train a specialized spoken utterance grammar correction model and a high-quality social chit-chat conversation model. We then conduct a preliminary user study with a variety of strategies for empathetic feedback. Our experiment suggests that using adaptive empathetic feedback leads to higher *perceived affective support*. Furthermore, elements of perceived affective support positively correlate with student grit.
Anthology ID:
2024.findings-emnlp.200
Volume:
Findings of the Association for Computational Linguistics: EMNLP 2024
Month:
November
Year:
2024
Address:
Miami, Florida, USA
Editors:
Yaser Al-Onaizan, Mohit Bansal, Yun-Nung Chen
Venue:
Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
3492–3511
Language:
URL:
https://aclanthology.org/2024.findings-emnlp.200
DOI:
Bibkey:
Cite (ACL):
Siyan Li, Teresa Shao, Zhou Yu, and Julia Hirschberg. 2024. EDEN: Empathetic Dialogues for English Learning. In Findings of the Association for Computational Linguistics: EMNLP 2024, pages 3492–3511, Miami, Florida, USA. Association for Computational Linguistics.
Cite (Informal):
EDEN: Empathetic Dialogues for English Learning (Li et al., Findings 2024)
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PDF:
https://aclanthology.org/2024.findings-emnlp.200.pdf