Abstract
This paper explores the impact of incorporating sentiment, emotion, and domain-specific lexicons into a transformer-based model for depression symptom estimation. Lexicon information is added by marking the words in the input transcripts of patient-therapist conversations as well as in social media posts. Overall results show that the introduction of external knowledge within pre-trained language models can be beneficial for prediction performance, while different lexicons show distinct behaviours depending on the targeted task. Additionally, new state-of-the-art results are obtained for the estimation of depression level over patient-therapist interviews.
Abstract (translated)
本文探讨了将情感、情感和领域特定词汇融入基于Transformer的抑郁症状估计模型的影响。词汇信息是通过在患者与治疗师对话的输入转录中以及社交媒体帖子中标记单词来添加的。总体结果表明，在预训练语言模型中引入外部知识可以提高预测性能，而不同的词汇表现出针对特定任务的独特行为。此外，本文还获得了评估抑郁程度的新颖结果，该结果仅在患者与治疗师对话中进行。
URL
https://arxiv.org/abs/2404.19359
