# Predicting Evoked Emotions in Conversations

arXiv: https://arxiv.org/abs/2401.00383 · HTML: https://mlsanity.com/paper/2401.00383/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 18 of its 31 references that are in the map.
- First posted on arXiv 31 Dec 2023.

## Builds on (most cited first, 18 of 18)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
- [Automated Hate Speech Detection and the Problem of Offensive Language](https://mlsanity.com/paper/1703.04009/) (2017)
- [DailyDialog: A Manually Labelled Multi-turn Dialogue Dataset](https://mlsanity.com/paper/1710.03957/) (2017)
- [MELD: A Multimodal Multi-Party Dataset for Emotion Recognition in Conversations](https://mlsanity.com/paper/1810.02508/) (2018)
- [DialogueGCN: A Graph Convolutional Neural Network for Emotion Recognition in Conversation](https://mlsanity.com/paper/1908.11540/) (2019)
- [Emotional Chatting Machine: Emotional Conversation Generation with Internal and External Memory](https://mlsanity.com/paper/1704.01074/) (2017)
- [Emotion Recognition in Conversation: Research Challenges, Datasets, and Recent Advances](https://mlsanity.com/paper/1905.02947/) (2019)
- [EmotionLines: An Emotion Corpus of Multi-Party Conversations](https://mlsanity.com/paper/1802.08379/) (2018)
- [Conversations Gone Awry: Detecting Early Signs of Conversational Failure](https://mlsanity.com/paper/1805.05345/) (2018)
- [MojiTalk: Generating Emotional Responses at Scale](https://mlsanity.com/paper/1711.04090/) (2017)
- [Affect-Driven Dialog Generation](https://mlsanity.com/paper/1904.02793/) (2019)
- [Affective Neural Response Generation](https://mlsanity.com/paper/1709.03968/) (2017)
- [Identifying the social signals that drive online discussions: A case study of Reddit communities](https://mlsanity.com/paper/1705.02673/) (2017)
- [An Adversarial Approach to High-Quality, Sentiment-Controlled Neural Dialogue Generation](https://mlsanity.com/paper/1901.07129/) (2019)
- [Generating Emotionally Aligned Responses in Dialogues using Affect Control Theory](https://mlsanity.com/paper/2003.03645/) (2020)
- [Using Sentiment Information for Preemptive Detection of Toxic Comments in Online Conversations](https://mlsanity.com/paper/2006.10145/) (2020)
- [Emotion Profile Refinery for Speech Emotion Classification](https://mlsanity.com/paper/2008.05259/) (2020)

## Abstract (arXiv)

Understanding and predicting the emotional trajectory in multi-party multi-turn conversations is of great significance. Such information can be used, for example, to generate empathetic response in human-machine interaction or to inform models of pre-emptive toxicity detection. In this work, we introduce the novel problem of Predicting Emotions in Conversations (PEC) for the next turn (n+1), given combinations of textual and/or emotion input up to turn n. We systematically approach the problem by modeling three dimensions inherently connected to evoked emotions in dialogues, including (i) sequence modeling, (ii) self-dependency modeling, and (iii) recency modeling. These modeling dimensions are then incorporated into two deep neural network architectures, a sequence model and a graph convolutional network model. The former is designed to capture the sequence of utterances in a dialogue, while the latter captures the sequence of utterances and the network formation of multi-party dialogues. We perform a comprehensive empirical evaluation of the various proposed models for addressing the PEC problem. The results indicate (i) the importance of the self-dependency and recency model dimensions for the prediction task, (ii) the quality of simpler sequence models in short dialogues, (iii) the importance of the graph neural models in improving the predictions in long dialogues.