  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.
