CLFeb 24, 2025

Moderation Matters:Measuring Conversational Moderation Impact in English as a Second Language Group Discussion

arXiv:2502.18341v14 citationsh-index: 36ACL
Originality Synthesis-oriented
AI Analysis

This work addresses engagement challenges for ESL learners in group discussions, but it is incremental as it builds on existing moderation research with a specific focus on ESL contexts.

The study tackled the problem of ESL speakers struggling in group discussions by developing a dataset and method to assess moderation effectiveness, finding that active acknowledgement and encouragement improved engagement while excessive moderator input had negative effects.

English as a Second Language (ESL) speakers often struggle to engage in group discussions due to language barriers. While moderators can facilitate participation, few studies assess conversational engagement and evaluate moderation effectiveness. To address this gap, we develop a dataset comprising 17 sessions from an online ESL conversation club, which includes both moderated and non-moderated discussions. We then introduce an approach that integrates automatic ESL dialogue assessment and a framework that categorizes moderation strategies. Our findings indicate that moderators help improve the flow of topics and start/end a conversation. Interestingly, we find active acknowledgement and encouragement to be the most effective moderation strategy, while excessive information and opinion sharing by moderators has a negative impact. Ultimately, our study paves the way for analyzing ESL group discussions and the role of moderators in non-native conversation settings.

Code Implementations1 repo
Foundations

The foundational work for this paper's niche, ranked by how specifically the neighbourhood builds on it — not by global fame.

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