  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.
