PT-CoDE: Pre-trained Context-Dependent Encoder for Utterance-level Emotion Recognition

Understand

Utterance-level emotion recognition (ULER) is a significant research topic for understanding human behaviors and developing empathetic chatting machines in the artificial intelligence area.

• Unlike traditional text classification problem, this task is supported by a limited number of datasets, among which most contain inadequate conversations or speeches.
• Such a data scarcity issue limits the possibility of training larger and more powerful models for this task.
• Witnessing the success of transfer learning in natural language process (NLP), we propose to pre-train a context-dependent encoder (CoDE) for ULER by learning from unlabeled conversation data.