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        <title>Multimodal Conversation Modelling for Topic Derailment Detection</title>
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        <namePart type="given">Zhenhao</namePart>
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    <name type="personal">
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            <namePart type="given">Yoav</namePart>
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    <abstract>Conversations on social media tend to go off-topic and turn into different and sometimes toxic exchanges. Previous work focuses on analysing textual dialogues that have derailed into toxic content, but the range of derailment types is much broader, including spam or bot content, tangential comments, etc. In addition, existing work disregards conversations that involve visual information (i.e. images or videos), which are prevalent on most platforms. In this paper, we take a broader view of conversation derailment and propose a new challenge: detecting derailment based on the “change of conversation topic”, where the topic is defined by an initial post containing both a text and an image. For that, we (i) create the first Multimodal Conversation Derailment (MCD) dataset, and (ii) introduce a new multimodal conversational architecture (MMConv) that utilises visual and conversational contexts to classify comments for derailment. Experiments show that MMConv substantially outperforms previous text-based approaches to detect conversation derailment, as well as general multimodal classifiers. MMConv is also more robust to textual noise, since it relies on richer contextual information.</abstract>
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    <identifier type="doi">10.18653/v1/2022.findings-emnlp.376</identifier>
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%0 Conference Proceedings
%T Multimodal Conversation Modelling for Topic Derailment Detection
%A Li, Zhenhao
%A Rei, Marek
%A Specia, Lucia
%Y Goldberg, Yoav
%Y Kozareva, Zornitsa
%Y Zhang, Yue
%S Findings of the Association for Computational Linguistics: EMNLP 2022
%D 2022
%8 December
%I Association for Computational Linguistics
%C Abu Dhabi, United Arab Emirates
%F li-etal-2022-multimodal
%X Conversations on social media tend to go off-topic and turn into different and sometimes toxic exchanges. Previous work focuses on analysing textual dialogues that have derailed into toxic content, but the range of derailment types is much broader, including spam or bot content, tangential comments, etc. In addition, existing work disregards conversations that involve visual information (i.e. images or videos), which are prevalent on most platforms. In this paper, we take a broader view of conversation derailment and propose a new challenge: detecting derailment based on the “change of conversation topic”, where the topic is defined by an initial post containing both a text and an image. For that, we (i) create the first Multimodal Conversation Derailment (MCD) dataset, and (ii) introduce a new multimodal conversational architecture (MMConv) that utilises visual and conversational contexts to classify comments for derailment. Experiments show that MMConv substantially outperforms previous text-based approaches to detect conversation derailment, as well as general multimodal classifiers. MMConv is also more robust to textual noise, since it relies on richer contextual information.
%R 10.18653/v1/2022.findings-emnlp.376
%U https://aclanthology.org/2022.findings-emnlp.376
%U https://doi.org/10.18653/v1/2022.findings-emnlp.376
%P 5115-5127

Markdown (Informal)

[Multimodal Conversation Modelling for Topic Derailment Detection](https://aclanthology.org/2022.findings-emnlp.376) (Li et al., Findings 2022)

• Multimodal Conversation Modelling for Topic Derailment Detection (Li et al., Findings 2022)

ACL

• Zhenhao Li, Marek Rei, and Lucia Specia. 2022. Multimodal Conversation Modelling for Topic Derailment Detection. In Findings of the Association for Computational Linguistics: EMNLP 2022, pages 5115–5127, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.