In this thesis, I tackle the problems in recently emerging technology-mediated psychotherapy through computational methods. Among them, I focus on developing natural language understanding techniques since verbal interaction is most important factor to understand the interaction. Specifically, I develop models and resources to understand languages of clients in counseling, languages of people in suicidal risk, languages of people in various emotional state.
For language understanding of clients in text-based counseling, I develop a categorization method as a labeling scheme for client utterances. I also propose a new model, Conversation Model Fine-Tuning to classify the utterances with small size of labeled data. This allows us to understand client's language and automatically extract meaningful information from them.
For language understanding of people in suicidal risk, I build a dataset of social media posts written by military personnel with corresponding expert annotations of suicidal risk levels. Various pretrained language models are further fine-tuned by using the dataset to classify the risks for developing simple yet effective baselines, achieving high classification performance. This could be applied to help them in time.
For language understanding of people in various emotional states, I propose a framework which enables a model to learn to predict dimensional emotions as well as categorical emotions, only trained from corpus annotated with categorical emotion labels, to give better emotional feedback in self-help psychotherapy without labeled data. Dimensional emotion predicted by a model trained using our framework shows significant positive correlations to corresponding ground truth without direct supervision.
Through these contributions, our knowledge could advance in understanding dynamics in technology-mediated psychotherapy and relevant information seeking behaviors of people in need. Clients and therapists could be supported in practice by automatized computational models as well.
전산심리치료에 적용할 수 있는 자연어 처리 모델의 학습 방법과 학습 데이터의 제작 방법을 다루었다. 첫째, 텍스트 기반 심리상담을 위한 내담자 발화의 유목화를 진행하고, 상담 대화록에 등장하는 내담자 발화에 각 유목의 등장 여부를 주석하여 학습 데이터를 제작하고, 사전학습된 대화 모델을 활용하여 유목 분류기를 학습하는 방법을 제시하였다. 둘째, 온라인 질의응답 플랫폼에서 등장하는 군인들의 자살 위험이 담긴 게시글을 수집하여 자살 위험 예측 모델을 학습하기 위한 학습 데이터를 구축하였다. 셋째, 심리치료 자활프로그램에서 자주 등장하는 회고 혹은 일기를 활용해 감정 추적을 수월하게 도와줄 수 있는 감정 인식 모델을 개발하였다. 기존 연구는 텍스트에서 기본 감정을 추출하는 모델과 학습 방법을 다루는데, 여기서 더 나아가 기본 감정이 주석된 학습 데이터를 활용해 연속적인 감정을 추론하는 모델의 학습 방법을 제시하고, 이를 일기 텍스트에 적용해 좀 더 풍부한 감정 추론이 가능함을 보였다.
