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.

翻译：本文探讨了将情感、情绪及领域特定词典融入基于Transformer的模型中对抑郁症症状估计的影响。通过在患者-治疗师对话记录及社交媒体帖子的输入文本中标记词语来添加词典信息。总体结果表明，在预训练语言模型中引入外部知识有助于提升预测性能，而不同词典根据目标任务表现出不同的行为特征。此外，在患者-治疗师访谈的抑郁程度评估中取得了新的最优结果。