# HiGRU: Hierarchical Gated Recurrent Units for Utterance-level Emotion Recognition

arXiv: https://arxiv.org/abs/1904.04446 · HTML: https://mlsanity.com/paper/1904.04446/

## Key facts

- Cited by 43 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Apr 2019.

## Built on by (most cited first, 30 of 43)

- [Directed Acyclic Graph Network for Conversational Emotion Recognition](https://mlsanity.com/paper/2105.12907/) (2021)
- [Topic-Driven and Knowledge-Aware Transformer for Dialogue Emotion Detection](https://mlsanity.com/paper/2106.01071/) (2021)
- [A Transformer-Based Model With Self-Distillation for Multimodal Emotion Recognition in Conversations](https://mlsanity.com/paper/2310.20494/) (2023)
- [Supervised Prototypical Contrastive Learning for Emotion Recognition in Conversation](https://mlsanity.com/paper/2210.08713/) (2022)
- [Natural Language Interfaces for Tabular Data Querying and Visualization: A Survey](https://mlsanity.com/paper/2310.17894/) (2023)
- [DialoGraph: Incorporating Interpretable Strategy-Graph Networks into Negotiation Dialogues](https://mlsanity.com/paper/2106.00920/) (2021)
- [Hybrid Curriculum Learning for Emotion Recognition in Conversation](https://mlsanity.com/paper/2112.11718/) (2021)
- [User Satisfaction Estimation with Sequential Dialogue Act Modeling in Goal-oriented Conversational Systems](https://mlsanity.com/paper/2202.02912/) (2022)
- [GraphCFC: A Directed Graph Based Cross-Modal Feature Complementation Approach for Multimodal Conversational Emotion Recognition](https://mlsanity.com/paper/2207.12261/) (2022)
- [RESPER: Computationally Modelling Resisting Strategies in Persuasive Conversations](https://mlsanity.com/paper/2101.10545/) (2021)
- [Modeling Multimodal Social Interactions: New Challenges and Baselines with Densely Aligned Representations](https://mlsanity.com/paper/2403.02090/) (2024)
- [TelME: Teacher-leading Multimodal Fusion Network for Emotion Recognition in Conversation](https://mlsanity.com/paper/2401.12987/) (2024)
- [BERT-ERC: Fine-tuning BERT is Enough for Emotion Recognition in Conversation](https://mlsanity.com/paper/2301.06745/) (2023)
- [Speaker-Guided Encoder-Decoder Framework for Emotion Recognition in Conversation](https://mlsanity.com/paper/2206.03173/) (2022)
- [Schema-Guided User Satisfaction Modeling for Task-Oriented Dialogues](https://mlsanity.com/paper/2305.16798/) (2023)
- [EmotionIC: emotional inertia and contagion-driven dependency modeling for emotion recognition in conversation](https://mlsanity.com/paper/2303.11117/) (2023)
- [Progressive Open-Domain Response Generation with Multiple Controllable Attributes](https://mlsanity.com/paper/2106.14614/) (2021)
- [TSAM: A Two-Stream Attention Model for Causal Emotion Entailment](https://mlsanity.com/paper/2203.00819/) (2022)
- [Modeling User Satisfaction Dynamics in Dialogue via Hawkes Process](https://mlsanity.com/paper/2305.12594/) (2023)
- [Akan Cinematic Emotions (ACE): A Multimodal Multi-party Dataset for Emotion Recognition in Movie Dialogues](https://mlsanity.com/paper/2502.10973/) (2025)
- [Emotion Dynamics Modeling via BERT](https://mlsanity.com/paper/2104.07252/) (2021)
- [A Unified One-Step Solution for Aspect Sentiment Quad Prediction](https://mlsanity.com/paper/2306.04152/) (2023)
- [SemEval 2024 -- Task 10: Emotion Discovery and Reasoning its Flip in Conversation (EDiReF)](https://mlsanity.com/paper/2402.18944/) (2024)
- [Emotion Flip Reasoning in Multiparty Conversations](https://mlsanity.com/paper/2306.13959/) (2023)
- [Unimodal-driven Distillation in Multimodal Emotion Recognition with Dynamic Fusion](https://mlsanity.com/paper/2503.23721/) (2025)
- [Dynamic Graph Neural ODE Network for Multi-modal Emotion Recognition in Conversation](https://mlsanity.com/paper/2412.02935/) (2024)
- [From Multilingual Complexity to Emotional Clarity: Leveraging Commonsense to Unveil Emotions in Code-Mixed Dialogues](https://mlsanity.com/paper/2310.13080/) (2023)
- [Large-Context Conversational Representation Learning: Self-Supervised Learning for Conversational Documents](https://mlsanity.com/paper/2102.08147/) (2021)
- [AdCOFE: Advanced Contextual Feature Extraction in Conversations for emotion classification](https://mlsanity.com/paper/2104.04517/) (2021)
- [MagicPai at SemEval-2021 Task 7: Method for Detecting and Rating Humor Based on Multi-Task Adversarial Training](https://mlsanity.com/paper/2104.10336/) (2021)

## Abstract (arXiv)

In this paper, we address three challenges in utterance-level emotion recognition in dialogue systems: (1) the same word can deliver different emotions in different contexts; (2) some emotions are rarely seen in general dialogues; (3) long-range contextual information is hard to be effectively captured. We therefore propose a hierarchical Gated Recurrent Unit (HiGRU) framework with a lower-level GRU to model the word-level inputs and an upper-level GRU to capture the contexts of utterance-level embeddings. Moreover, we promote the framework to two variants, HiGRU with individual features fusion (HiGRU-f) and HiGRU with self-attention and features fusion (HiGRU-sf), so that the word/utterance-level individual inputs and the long-range contextual information can be sufficiently utilized. Experiments on three dialogue emotion datasets, IEMOCAP, Friends, and EmotionPush demonstrate that our proposed HiGRU models attain at least 8.7%, 7.5%, 6.0% improvement over the state-of-the-art methods on each dataset, respectively. Particularly, by utilizing only the textual feature in IEMOCAP, our HiGRU models gain at least 3.8% improvement over the state-of-the-art conversational memory network (CMN) with the trimodal features of text, video, and audio.