Leveraging Sentiment Analysis Knowledge to Solve Emotion Detection Tasks
This work addresses the challenge of emotion detection in natural language processing, which is important for applications requiring nuanced understanding of text, but it appears incremental as it builds on existing Transformer-based methods with adapter layers.
The paper tackled the problem of identifying complex, finer-grained emotions in text by leveraging knowledge from simpler sentiment analysis tasks, achieving state-of-the-art results for emotion recognition on the CMU-MOSEI dataset using only textual data.
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.