Summary of Knowledge-aware Conversation Derailment Forecasting Using Graph Convolutional Networks, by Enas Altarawneh et al.
Knowledge-Aware Conversation Derailment Forecasting Using Graph Convolutional Networks
by Enas Altarawneh, Ameeta Agrawal, Michael Jenkin, Manos Papagelis
First submitted to arxiv on: 24 Aug 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes an innovative approach to forecasting online conversations’ potential for derailment, enabling proactive moderation to mitigate toxic communication patterns. The authors leverage common sense knowledge to enrich graph neural networks, capturing complex conversational characteristics like context propagation and emotional shifts. By deriving commonsense statements from a dialogue contextual information knowledge base, the model fuses multi-source utterance information into capsules, which are then used by a transformer-based forecaster to predict derailment. The proposed approach outperforms state-of-the-art models on benchmark datasets CGA and CMV. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you’re chatting online with friends, but the conversation starts going wrong. This paper helps anticipate when that might happen, so moderators can step in earlier to keep things civil. The team developed a new way to understand online conversations by combining common sense ideas with special computer algorithms. It’s like having a superpower to predict when a chat might get out of hand! They tested this approach on two big datasets and it worked better than what’s currently available. |
Keywords
» Artificial intelligence » Knowledge base » Transformer
