# A Self-Attentive Emotion Recognition Network

## TL;DR

This paper introduces a novel self-attention based neural network for emotion recognition in conversations, effectively modeling long-term temporal dependencies without decoder networks, and outperforms existing methods on the IEMOCAP benchmark.

## Contribution

It proposes a new self-attention mechanism that captures long-range emotional dynamics without requiring a decoder, advancing emotion recognition in dyadic conversations.

## Key findings

- Outperforms state-of-the-art methods on IEMOCAP
- Effectively models long-term temporal dependencies
- Provides a new direction for social network analysis

## Abstract

Modern deep learning approaches have achieved groundbreaking performance in modeling and classifying sequential data. Specifically, attention networks constitute the state-of-the-art paradigm for capturing long temporal dynamics. This paper examines the efficacy of this paradigm in the challenging task of emotion recognition in dyadic conversations. In contrast to existing approaches, our work introduces a novel attention mechanism capable of inferring the immensity of the effect of each past utterance on the current speaker emotional state. The proposed attention mechanism performs this inference procedure without the need of a decoder network; this is achieved by means of innovative self-attention arguments. Our self-attention networks capture the correlation patterns among consecutive encoder network states, thus allowing to robustly and effectively model temporal dynamics over arbitrary long temporal horizons. Thus, we enable capturing strong affective patterns over the course of long discussions. We exhibit the effectiveness of our approach considering the challenging IEMOCAP benchmark. As we show, our devised methodology outperforms state-of-the-art alternatives and commonly used approaches, giving rise to promising new research directions in the context of Online Social Network (OSN) analysis tasks.