BERT 4EVER@LT-EDI-ACL2022-Detecting signs of Depression from Social Media:Detecting Depression in Social Media using Prompt-Learning and Word-Emotion Cluster
Xiaotian Lin, Yingwen Fu, Ziyu Yang, Nankai Lin, Shengyi Jiang
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In this paper, we report the solution of the team BERT 4EVER for the LT-EDI-2022 shared task2: Homophobia/Transphobia Detection in social media comments in ACL 2022, which aims to classify Youtube comments into one of the following categories: no,moderate, or severe depression. We model the problem as a text classification task and a text generation task and respectively propose two different models for the tasks.To combine the knowledge learned from these two different models, we softly fuse the predicted probabilities of the models above and then select the label with the highest probability as the final output.In addition, multiple augmentation strategies are leveraged to improve the model generalization capability, such as back translation and adversarial training.Experimental results demonstrate the effectiveness of the proposed models and two augmented strategies.