# Using Knowledge-Embedded Attention to Augment Pre-trained Language Models for Fine-Grained Emotion Recognition

arXiv: https://arxiv.org/abs/2108.00194 · HTML: https://mlsanity.com/paper/2108.00194/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 28 of its 49 references that are in the map.
- First posted on arXiv 31 Jul 2021.

## Builds on (most cited first, 28 of 28)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [ConceptNet 5.5: An Open Multilingual Graph of General Knowledge](https://mlsanity.com/paper/1612.03975/) (2016)
- [ERNIE: Enhanced Language Representation with Informative Entities](https://mlsanity.com/paper/1905.07129/) (2019)
- [GoEmotions: A Dataset of Fine-Grained Emotions](https://mlsanity.com/paper/2005.00547/) (2020)
- [K-Adapter: Infusing Knowledge into Pre-Trained Models with Adapters](https://mlsanity.com/paper/2002.01808/) (2020)
- [A Structured Self-attentive Sentence Embedding](https://mlsanity.com/paper/1703.03130/) (2017)
- [Challenges in Building Intelligent Open-domain Dialog Systems](https://mlsanity.com/paper/1905.05709/) (2019)
- [Emotion Recognition in Conversation: Research Challenges, Datasets, and Recent Advances](https://mlsanity.com/paper/1905.02947/) (2019)
- [KG-BART: Knowledge Graph-Augmented BART for Generative Commonsense Reasoning](https://mlsanity.com/paper/2009.12677/) (2020)
- [E-BERT: Efficient-Yet-Effective Entity Embeddings for BERT](https://mlsanity.com/paper/1911.03681/) (2019)
- [Knowledge-Enriched Transformer for Emotion Detection in Textual Conversations](https://mlsanity.com/paper/1909.10681/) (2019)
- [SenseBERT: Driving Some Sense into BERT](https://mlsanity.com/paper/1908.05646/) (2019)
- [SKEP: Sentiment Knowledge Enhanced Pre-training for Sentiment Analysis](https://mlsanity.com/paper/2005.05635/) (2020)
- [Word Affect Intensities](https://mlsanity.com/paper/1704.08798/) (2017)
- [Real-Time Emotion Recognition via Attention Gated Hierarchical Memory Network](https://mlsanity.com/paper/1911.09075/) (2019)
- [NTUA-SLP at SemEval-2018 Task 1: Predicting Affective Content in Tweets with Deep Attentive RNNs and Transfer Learning](https://mlsanity.com/paper/1804.06658/) (2018)
- [Enriching BERT with Knowledge Graph Embeddings for Document Classification](https://mlsanity.com/paper/1909.08402/) (2019)
- [Modeling emotion in complex stories: the Stanford Emotional Narratives Dataset](https://mlsanity.com/paper/1912.05008/) (2019)
- [Context-Guided BERT for Targeted Aspect-Based Sentiment Analysis](https://mlsanity.com/paper/2010.07523/) (2020)
- [From Standard Summarization to New Tasks and Beyond: Summarization with Manifold Information](https://mlsanity.com/paper/2005.04684/) (2020)
- [EmotionX-IDEA: Emotion BERT -- an Affectional Model for Conversation](https://mlsanity.com/paper/1908.06264/) (2019)
- [Lexicon Integrated CNN Models with Attention for Sentiment Analysis](https://mlsanity.com/paper/1610.06272/) (2016)
- [Attention-based Conditioning Methods for External Knowledge Integration](https://mlsanity.com/paper/1906.03674/) (2019)
- [Attending to Emotional Narratives](https://mlsanity.com/paper/1907.04197/) (2019)
- [An Ethical Framework for Guiding the Development of Affectively-Aware Artificial Intelligence](https://mlsanity.com/paper/2107.13734/) (2021)
- [Joint Emotion Label Space Modelling for Affect Lexica](https://mlsanity.com/paper/1911.08782/) (2019)

## Built on by (most cited first, 10 of 10)

- [Not All Negatives are Equal: Label-Aware Contrastive Loss for Fine-grained Text Classification](https://mlsanity.com/paper/2109.05427/) (2021)
- [Large Language Models Produce Responses Perceived to be Empathic](https://mlsanity.com/paper/2403.18148/) (2024)
- [Theory of Mind abilities of Large Language Models in Human-Robot Interaction : An Illusion?](https://mlsanity.com/paper/2401.05302/) (2024)
- [Natural Language Processing with Commonsense Knowledge: A Survey](https://mlsanity.com/paper/2108.04674/) (2021)
- [Using Positive Matching Contrastive Loss with Facial Action Units to mitigate bias in Facial Expression Recognition](https://mlsanity.com/paper/2303.04896/) (2023)
- [Representing Affect Information in Word Embeddings](https://mlsanity.com/paper/2209.10583/) (2022)
- [Retrofitting Light-weight Language Models for Emotions using Supervised Contrastive Learning](https://mlsanity.com/paper/2310.18930/) (2023)
- [Lotus at SemEval-2025 Task 11: RoBERTa with Llama-3 Generated Explanations for Multi-Label Emotion Classification](https://mlsanity.com/paper/2502.19935/) (2025)
- [TamilEmo: Finegrained Emotion Detection Dataset for Tamil](https://mlsanity.com/paper/2202.04725/) (2022)
- [A Comparative Study on Affective Cues in Text Embeddings Across Psychological Emotion Theories](https://mlsanity.com/paper/2606.29068/) (2026)

## Abstract (arXiv)

Modern emotion recognition systems are trained to recognize only a small set of emotions, and hence fail to capture the broad spectrum of emotions people experience and express in daily life. In order to engage in more empathetic interactions, future AI has to perform textitfine-grained emotion recognition, distinguishing between many more varied emotions. Here, we focus on improving fine-grained emotion recognition by introducing external knowledge into a pre-trained self-attention model. We propose Knowledge-Embedded Attention (KEA) to use knowledge from emotion lexicons to augment the contextual representations from pre-trained ELECTRA and BERT models. Our results and error analyses outperform previous models on several datasets, and is better able to differentiate closely-confusable emotions, such as afraid and terrified.