SSLCL: A Computationally Efficient and Model-Agnostic Supervised Contrastive Learning Framework for Emotion Recognition in Conversations

Introduction

Supervised Sample-Label Contrastive Learning with Soft Hirschfeld-Gebelein-R{'e}nyi Maximal Correlation (SSLCL) is an efficient and model-agnostic supervised contrastive learning framework for the problem of Emotion Recognition in Conversations (ERC), which eliminates the need for a large batch size and can be seamlessly integrated with existing ERC models without introducing any model-specific assumptions. Extensive experiments on two ERC benchmark datasets, IEMOCAP and MELD, demonstrate the compatibility and superiority of our proposed SSLCL framework compared to existing state-of-the-art supervised contrastive learning (SCL) methods.

The full paper is available at https://arxiv.org/abs/2310.16676.

Model Architecture

The overall framework of SSLCL is illustrated as follows, which is made up of three key components: sample feature extraction, label learning, and sample-label contrastive learning.

Results

The comparisons between SSLCL and existing SCL approaches on IEMOCAP and MELD are shown as follows.

IEMOCAP

Model	Happy	Sad	Neutral	Angry	Excited	Frustrated	Weighted-F1
M3Net + CE	52.74	79.39	67.55	69.30	74.39	66.58	69.24
M3Net + SupCon	48.80	80.17	66.67	67.68	75.62	66.58	68.86
M3Net + mv-SupCon	51.23	80.26	66.17	69.01	69.40	67.25	68.12
M3Net + SWFC	54.67	80.85	68.61	67.42	76.92	62.41	69.17
M3Net + SSLCL	58.44	82.43	69.32	71.44	77.02	69.34	71.98

MELD

Model	Neutral	Surprise	Fear	Sad	Happy	Disgust	Anger	Weighted-F1
M3Net + CE	79.31	58.76	20.51	40.46	63.21	26.17	52.53	65.47
M3Net + SupCon	78.58	59.50	23.19	39.56	65.04	23.16	52.70	65.40
M3Net + mv-SupCon	78.11	59.72	23.08	42.05	63.60	23.53	53.91	65.34
M3Net + SWFC	78.08	60.31	25.26	39.48	64.12	29.33	53.57	65.42
M3Net + SSLCL	79.73	61.03	27.32	42.46	65.08	31.30	54.76	66.92

Citation

If you find our work helpful to your research, please cite our paper as follows.

@misc{shi2023sslcl,
      title={SSLCL: An Efficient Model-Agnostic Supervised Contrastive Learning Framework for Emotion Recognition in Conversations}, 
      author={Tao Shi and Xiao Liang and Yaoyuan Liang and Xinyi Tong and Shao-Lun Huang},
      year={2023},
      eprint={2310.16676},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}