Prompt Consistency for Multi-Label Textual Emotion Detection
Yangyang Zhou, Xin Kang, Fuji Ren
Abstract
Textual emotion detection is playing an important role in the human-computer interaction domain. The mainstream methods of textual emotion detection are extracting semantic features and fine-tuning by language models. Due to the information redundancy in semantics, it is difficult for these methods to accurately detect all the emotions implied in the text. The prompting method has been shown to make the language models more purposeful in prediction by filling the cloze or prefix prompts defined. Therefore, we design a prompting method for multi-label classification. To stabilize the output, we design two consistency training strategies. We experiment on two multi-label emotion classification datasets: Ren-CECps and NLPCC2018. Our proposed prompting method with consistency training strategies for multi-label textual emotion detection (PC-MTED) model achieves state-of-the-art Macro F1 scores of 0.5432 and 0.5269, respectively. The experimental results indicate that our proposed method is effective in the multi-label textual emotion detection task.