ERNetCL: A novel emotion recognition network in textual conversation based on curriculum learning strategy

• URL: http://arxiv.org/abs/2308.06450v2
• Date: Thu, 25 Jan 2024 09:42:40 GMT
• Title: ERNetCL: A novel emotion recognition network in textual conversation based on curriculum learning strategy
• Authors: Jiang Li, Xiaoping Wang, Yingjian Liu, Zhigang Zeng
• Abstract summary: We propose a novel emotion recognition network based on curriculum learning strategy (ERNetCL) The proposed ERNetCL primarily consists of temporal encoder (TE), spatial encoder (SE), and curriculum learning (CL) loss. Our proposed method is effective and dramatically beats other baseline models.
• Score: 37.41082775317849
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Emotion recognition in conversation (ERC) has emerged as a research hotspot in domains such as conversational robots and question-answer systems. How to efficiently and adequately retrieve contextual emotional cues has been one of the key challenges in the ERC task. Existing efforts do not fully model the context and employ complex network structures, resulting in limited performance gains. In this paper, we propose a novel emotion recognition network based on curriculum learning strategy (ERNetCL). The proposed ERNetCL primarily consists of temporal encoder (TE), spatial encoder (SE), and curriculum learning (CL) loss. We utilize TE and SE to combine the strengths of previous methods in a simplistic manner to efficiently capture temporal and spatial contextual information in the conversation. To ease the harmful influence resulting from emotion shift and simulate the way humans learn curriculum from easy to hard, we apply the idea of CL to the ERC task to progressively optimize the network parameters. At the beginning of training, we assign lower learning weights to difficult samples. As the epoch increases, the learning weights for these samples are gradually raised. Extensive experiments on four datasets exhibit that our proposed method is effective and dramatically beats other baseline models.

Related papers

• Semantic Meta-Split Learning: A TinyML Scheme for Few-Shot Wireless Image Classification [50.28867343337997]
  This work presents a TinyML-based semantic communication framework for few-shot wireless image classification. We exploit split-learning to limit the computations performed by the end-users while ensuring privacy-preserving. meta-learning overcomes data availability concerns and speeds up training by utilizing similarly trained tasks.
  arXiv  Detail & Related papers  (2024-09-03T05:56:55Z)
• Contrastive Augmentation: An Unsupervised Learning Approach for Keyword Spotting in Speech Technology [4.080686348274667]
  We introduce a novel approach combining unsupervised contrastive learning and a augmentation unique-based technique. Our method allows the neural network to train on unlabeled data sets, potentially improving performance in downstream tasks. We present a speech augmentation-based unsupervised learning method that utilizes the similarity between the bottleneck layer feature and the audio reconstructing information.
  arXiv  Detail & Related papers  (2024-08-31T05:40:37Z)
• Text-Video Retrieval with Global-Local Semantic Consistent Learning [122.15339128463715]
  We propose a simple yet effective method, Global-Local Semantic Consistent Learning (GLSCL) GLSCL capitalizes on latent shared semantics across modalities for text-video retrieval. Our method achieves comparable performance with SOTA as well as being nearly 220 times faster in terms of computational cost.
  arXiv  Detail & Related papers  (2024-05-21T11:59:36Z)
• Curriculum Learning Meets Directed Acyclic Graph for Multimodal Emotion Recognition [2.4660652494309936]
  MultiDAG+CL is a novel approach for Multimodal Emotion Recognition in Conversation (ERC) The model is enhanced by Curriculum Learning (CL) to address challenges related to emotional shifts and data imbalance. Experimental results on the IEMOCAP and MELD datasets demonstrate that the MultiDAG+CL models outperform baseline models.
  arXiv  Detail & Related papers  (2024-02-27T07:28:05Z)
• Supervised Prototypical Contrastive Learning for Emotion Recognition in Conversation [25.108385802645163]
  We propose a Supervised Prototypical Contrastive Learning (SPCL) loss for the emotion recognition task. We design a difficulty measure function based on the distance between classes and introduce curriculum learning to alleviate the impact of extreme samples. We achieve state-of-the-art results on three widely used benchmarks.
  arXiv  Detail & Related papers  (2022-10-17T03:08:23Z)
• Improved Speech Emotion Recognition using Transfer Learning and Spectrogram Augmentation [56.264157127549446]
  Speech emotion recognition (SER) is a challenging task that plays a crucial role in natural human-computer interaction. One of the main challenges in SER is data scarcity. We propose a transfer learning strategy combined with spectrogram augmentation.
  arXiv  Detail & Related papers  (2021-08-05T10:39:39Z)
• Speech Command Recognition in Computationally Constrained Environments with a Quadratic Self-organized Operational Layer [92.37382674655942]
  We propose a network layer to enhance the speech command recognition capability of a lightweight network. The employed method borrows the ideas of Taylor expansion and quadratic forms to construct a better representation of features in both input and hidden layers. This richer representation results in recognition accuracy improvement as shown by extensive experiments on Google speech commands (GSC) and synthetic speech commands (SSC) datasets.
  arXiv  Detail & Related papers  (2020-11-23T14:40:18Z)
• Learning to Continuously Optimize Wireless Resource In Episodically Dynamic Environment [55.91291559442884]
  This work develops a methodology that enables data-driven methods to continuously learn and optimize in a dynamic environment. We propose to build the notion of continual learning into the modeling process of learning wireless systems. Our design is based on a novel min-max formulation which ensures certain fairness" across different data samples.
  arXiv  Detail & Related papers  (2020-11-16T08:24:34Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.