Conversations Gone Awry: Detecting Early Signs of Conversational Failure (P18-1)
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Justine Zhang, Jonathan Chang, Cristian Danescu-Niculescu-Mizil, Lucas Dixon, Yiqing Hua, Dario Taraborelli, Nithum Thain
| Challenge: | Prior work focused on characterizing and detecting content exhibiting antisocial online behavior. |
| Approach: | They propose a task of predicting from the very start of a conversation whether it will get out of hand. |
| Outcome: | The proposed framework can detect early warning signs of antisocial behavior in online conversations. |
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| Challenge: | Recent efforts focused on detecting antisocial behavior after the fact . a forecasting model needs to capture the flow of the conversation, not individual comments . real conversations have an unknown horizon; therefore a practical forecasting system needs to assess the risk . |
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A Theoretically Grounded Approach to Summarizing Conversation Dynamics for Forecasting the Derailment of Online Conversations (2026.acl-long)
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| Challenge: | Recent work on conversation derailment prediction relies on linguistic features rooted in linguistic and social theories. |
| Approach: | They propose a system that predicts from the start of a conversation whether it will derail into toxic exchanges. |
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How did we get here? Summarizing conversation dynamics (2024.naacl-long)
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Yilun Hua, Nicholas Chernogor, Yuzhe Gu, Seoyeon Jeong, Miranda Luo, Cristian Danescu-Niculescu-Mizil
| Challenge: | Throughout a conversation, the way participants interact with each other is in constant flux. |
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Can LLMs Be Efficient Predictors of Conversational Derailment? (2025.findings-emnlp)
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| Challenge: | Conversational derailment is a common issue on online platforms due to toxic or inappropriate remarks. |
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Early Rumour Detection (N19-1)
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| Challenge: | Existing studies on rumour detection are concerned with timing, but few are interested in how early we can detect them. |
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Sketching a Linguistically-Driven Reasoning Dialog Model for Social Talk (2022.acl-srw)
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| Challenge: | a new study shows that dialog systems that can hold social talk and make sense of conversational content are not efficient for context-sensitive natural language understanding and reasoning. |
| Approach: | They propose a linguistically-informed architecture to handle social talk in English . they propose linguistic models that fit the context-sensitive components into a Bayesian game-theoretic model . |
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ProsocialDialog: A Prosocial Backbone for Conversational Agents (2022.emnlp-main)
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Hyunwoo Kim, Youngjae Yu, Liwei Jiang, Ximing Lu, Daniel Khashabi, Gunhee Kim, Yejin Choi, Maarten Sap
| Challenge: | Existing dialogue systems fail to respond properly to potentially unsafe user utterances . existing systems either ignore or passively agree with unsafe content . |
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SaFeRDialogues: Taking Feedback Gracefully after Conversational Safety Failures (2022.acl-long)
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| Challenge: | Existing open-domain conversational models can easily be made to talk in inadequate ways. |
| Approach: | They propose a task and dataset of graceful responses to safety feedback . they collect 8k dialogues demonstrating safety failures, feedback signaling them, and a response acknowledging feedback. |
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SafetyKit: First Aid for Measuring Safety in Open-domain Conversational Systems (2022.acl-long)
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| Challenge: | Several studies discuss the potential harms and benefits of large language models (LLMs) large neural models can replicate and even amplify negative, stereotypical, and derogatory associations in the data. |
| Approach: | They propose to use a first aid kit to assess the safety of conversational AI in various settings . they propose several future directions and discuss ethical considerations . |
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Wait! There’s a Way Out: A Decision Mechanism for Forecasting Conversational Derailment (2026.acl-long)
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| Challenge: | Existing approaches make decision to "trigger" based on the estimated likelihood of derailment given the preceding utterances, implicitly assuming that the conversation’s future trajectory is fixed. |
| Approach: | They propose a method for decoupling the decision to trigger from derailment likelihood estimation. |
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