# Leveraging Sentiment Analysis Knowledge to Solve Emotion Detection Tasks

arXiv: https://arxiv.org/abs/2111.03715 · HTML: https://mlsanity.com/paper/2111.03715/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 25 references that are in the map.
- First posted on arXiv 5 Nov 2021.

## Builds on (most cited first, 12 of 12)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Parameter-Efficient Transfer Learning for NLP](https://mlsanity.com/paper/1902.00751/) (2019)
- [XLNet: Generalized Autoregressive Pretraining for Language Understanding](https://mlsanity.com/paper/1906.08237/) (2019)
- [Decoupled Weight Decay Regularization](https://mlsanity.com/paper/1711.05101/) (2017)
- [AdapterFusion: Non-Destructive Task Composition for Transfer Learning](https://mlsanity.com/paper/2005.00247/) (2020)
- [Focal Loss for Dense Object Detection](https://mlsanity.com/paper/1708.02002/) (2017)
- [AdapterHub: A Framework for Adapting Transformers](https://mlsanity.com/paper/2007.07779/) (2020)
- [Multi-Task Deep Neural Networks for Natural Language Understanding](https://mlsanity.com/paper/1901.11504/) (2019)
- [BAM! Born-Again Multi-Task Networks for Natural Language Understanding](https://mlsanity.com/paper/1907.04829/) (2019)
- [A Transformer-based joint-encoding for Emotion Recognition and Sentiment Analysis](https://mlsanity.com/paper/2006.15955/) (2020)
- [Improving Multi-Task Deep Neural Networks via Knowledge Distillation for Natural Language Understanding](https://mlsanity.com/paper/1904.09482/) (2019)

## Built on by (most cited first, 1 of 1)

- [Adapter-based Approaches to Knowledge-enhanced Language Models -- A Survey](https://mlsanity.com/paper/2411.16403/) (2024)

## Abstract (arXiv)

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.