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

TL;DR

This paper introduces HiGRU, a hierarchical Gated Recurrent Unit framework for utterance-level emotion recognition in dialogue systems, effectively capturing context and handling rare emotions, leading to significant performance improvements.

Contribution

The paper proposes a novel hierarchical GRU model with variants that incorporate self-attention and feature fusion for improved emotion recognition in dialogues.

Findings

Achieves at least 8.7% improvement over state-of-the-art on IEMOCAP

Attains 7.5% and 6.0% improvements on Friends and EmotionPush datasets

Text-only HiGRU surpasses multimodal CMN in IEMOCAP

Abstract

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.