Text-based classification of interviews for mental health -- juxtaposing the state of the art
This work addresses the problem of mental health diagnosis using text data, particularly for Dutch language applications, but is incremental as it builds on existing RoBERTa architecture and explores hybrid approaches.
The paper tackled the classification of psychiatric illness by developing a new Dutch language model, belabBERT, and evaluating text-based classification against audio-based methods, achieving competitive performance as a stand-alone solution.
Currently, the state of the art for classification of psychiatric illness is based on audio-based classification. This thesis aims to design and evaluate a state of the art text classification network on this challenge. The hypothesis is that a well designed text-based approach poses a strong competition against the state-of-the-art audio based approaches. Dutch natural language models are being limited by the scarcity of pre-trained monolingual NLP models, as a result Dutch natural language models have a low capture of long range semantic dependencies over sentences. For this issue, this thesis presents belabBERT, a new Dutch language model extending the RoBERTa[15] architecture. belabBERT is trained on a large Dutch corpus (+32GB) of web crawled texts. After this thesis evaluates the strength of text-based classification, a brief exploration is done, extending the framework to a hybrid text- and audio-based classification. The goal of this hybrid framework is to show the principle of hybridisation with a very basic audio-classification network. The overall goal is to create the foundations for a hybrid psychiatric illness classification, by proving that the new text-based classification is already a strong stand-alone solution.