ACL 2025finding0 citations

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

Rena Gao, Ming-Bin Chen, Lea Frermann, Jey Han Lau

Abstract

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 and data are available at https://github.com/RenaGao/L2Moderator.

BibTeX
@inproceedings{gao-etal-2025-moderation,
    title = "Moderation Matters: Measuring Conversational Moderation Impact in {E}nglish as a Second Language Group Discussion",
    author = "Gao, Rena  and
      Chen, Ming-Bin  and
      Frermann, Lea  and
      Lau, Jey Han",
    editor = "Che, Wanxiang  and
      Nabende, Joyce  and
      Shutova, Ekaterina  and
      Pilehvar, Mohammad Taher",
    booktitle = "Findings of the Association for Computational Linguistics: ACL 2025",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2025.findings-acl.106/",
    doi = "10.18653/v1/2025.findings-acl.106",
    pages = "2070--2095",
    ISBN = "979-8-89176-256-5"
}