Leveraging Sentiment Analysis Knowledge to Solve Emotion Detection Tasks

TL;DR

This paper introduces a Transformer-based model with Adapter layers that leverages sentiment analysis knowledge to enhance emotion detection from text, achieving state-of-the-art results on CMU-MOSEI using only textual data.

Contribution

The paper proposes a novel Fusion of Adapter layers in a Transformer model to transfer knowledge from sentiment analysis to emotion detection tasks.

Findings

Achieved state-of-the-art emotion recognition results on CMU-MOSEI.

Model performs competitively with other approaches using only text.

Leveraging sentiment knowledge improves emotion detection accuracy.

Abstract

Identifying and understanding underlying sentiment or emotions in text is a key component of multiple natural language processing applications. While simple polarity sentiment analysis is a well-studied subject, fewer advances have been made in identifying more complex, finer-grained emotions using only textual data. In this paper, we present a Transformer-based model with a Fusion of Adapter layers which leverages knowledge from more simple sentiment analysis tasks to improve the emotion detection task on large scale dataset, such as CMU-MOSEI, using the textual modality only. Results show that our proposed method is competitive with other approaches. We obtained state-of-the-art results for emotion recognition on CMU-MOSEI even while using only the textual modality.