Deep learning for language understanding of mental health concepts derived from Cognitive Behavioural Therapy
This work addresses the challenge of automating therapy delivery through dialogue systems for mental health applications, though it appears incremental as it applies existing deep learning methods to a new domain-specific task.
The paper tackled the problem of understanding mental health concepts from Cognitive Behavioural Therapy (CBT) using deep learning, and found that deep learning models with word or sentence embeddings significantly outperformed non-deep-learning models in this task.
In recent years, we have seen deep learning and distributed representations of words and sentences make impact on a number of natural language processing tasks, such as similarity, entailment and sentiment analysis. Here we introduce a new task: understanding of mental health concepts derived from Cognitive Behavioural Therapy (CBT). We define a mental health ontology based on the CBT principles, annotate a large corpus where this phenomena is exhibited and perform understanding using deep learning and distributed representations. Our results show that the performance of deep learning models combined with word embeddings or sentence embeddings significantly outperform non-deep-learning models in this difficult task. This understanding module will be an essential component of a statistical dialogue system delivering therapy.