# Attending to Emotional Narratives

arXiv: https://arxiv.org/abs/1907.04197 · HTML: https://mlsanity.com/paper/1907.04197/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Jul 2019.

## Built on by (most cited first, 5 of 5)

- [Transformer-Based Self-Supervised Learning for Emotion Recognition](https://mlsanity.com/paper/2204.05103/) (2022)
- [Using Knowledge-Embedded Attention to Augment Pre-trained Language Models for Fine-Grained Emotion Recognition](https://mlsanity.com/paper/2108.00194/) (2021)
- [Leveraging Contrastive Learning and Self-Training for Multimodal Emotion Recognition with Limited Labeled Samples](https://mlsanity.com/paper/2409.04447/) (2024)
- [Automatic Emotion Modelling in Written Stories](https://mlsanity.com/paper/2212.11382/) (2022)
- [Leveraging Vision Transformers for Enhanced Classification of Emotions using ECG Signals](https://mlsanity.com/paper/2510.05826/) (2025)

## Abstract (arXiv)

Attention mechanisms in deep neural networks have achieved excellent performance on sequence-prediction tasks. Here, we show that these recently-proposed attention-based mechanisms---in particular, the Transformer with its parallelizable self-attention layers, and the Memory Fusion Network with attention across modalities and time---also generalize well to multimodal time-series emotion recognition. Using a recently-introduced dataset of emotional autobiographical narratives, we adapt and apply these two attention mechanisms to predict emotional valence over time. Our models perform extremely well, in some cases reaching a performance comparable with human raters. We end with a discussion of the implications of attention mechanisms to affective computing.