Summary of Predicting Evoked Emotions in Conversations, by Enas Altarawneh et al.
Predicting Evoked Emotions in Conversations
by Enas Altarawneh, Ameeta Agrawal, Michael Jenkin, Manos Papagelis
First submitted to arxiv on: 31 Dec 2023
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 The paper introduces a novel problem called Predicting Emotions in Conversations (PEC), which aims to predict emotions in multi-party conversations given input up to the current turn. The authors propose three modeling dimensions: sequence, self-dependency, and recency, which are incorporated into deep neural network architectures. They evaluate their models on a comprehensive empirical study, finding that self-dependency and recency model dimensions are crucial for predicting emotions, while simpler sequence models perform well in short dialogues, and graph neural models improve predictions in long dialogues. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper talks about how to understand what people are feeling when they’re having conversations with others. It’s important because it could help make computers respond more like humans do – by being empathetic. The authors came up with a new way to predict emotions in these conversations, using three key ideas: looking at the order of things said, how each person relates to themselves and others, and what happened most recently. They tested their idea using special computer models and found that it worked really well. |
Keywords
* Artificial intelligence * Neural network
