Motivational interviewing (MI) is a counselling style often used in healthcare to improve patient health and quality of life by promoting positive behaviour changes. Natural language processing (NLP) has been explored for supporting MI use cases of insights/feedback generation and therapist training, such as automatically assigning behaviour labels to therapist/client utterances and generating possible therapist responses.
Despite the progress of NLP for MI applications, significant challenges remain. The most prominent one is the lack of publicly available and annotated MI dialogue corpora due to privacy constraints. Consequently, there is also a lack of common benchmarks and poor reproducibility across studies. Furthermore, human evaluation for therapist response generation is expensive and difficult to scale due to its dependence on MI experts as evaluators. In this thesis, we address these challenges in 4 directions: low-resource NLP modelling, MI dialogue dataset creation, benchmark development for real-world applicable tasks, and laypeople-experts human evaluation study.
First, we explore zero-shot binary empathy assessment at the utterance level. We experiment with a supervised approach that trains on heuristically constructed empathy vs. non-empathy contrast in non-therapy dialogues. While this approach has better performance than other models without empathy-aware training, it is still suboptimal and therefore highlights the need for a well-annotated MI dataset.
Next, we create AnnoMI, the first publicly available dataset of expert-annotated MI dialogues. It contains MI conversations that demonstrate both high- and low-quality counselling, with extensive annotations by domain experts covering key MI attributes. We also conduct comprehensive analyses of the dataset.
Then, we investigate two AnnoMI-based real-world applicable tasks: predicting current-turn therapist/client behaviour given the utterance, and forecasting next-turn therapist behaviour given the dialogue history. We find that language models (LMs) perform well on predicting therapist behaviours with good generalisability to new dialogue topics. However, LMs have suboptimal forecasting performance, which reflects therapists' flexibility where multiple optimal next-turn actions are possible.
Lastly, we ask both laypeople and experts to evaluate the generation of a crucial type of therapist responses -- reflection -- on a key quality aspect: coherence and context-consistency. We find that laypeople are a viable alternative to experts, as laypeople show good agreement with each other and correlation with experts. We also find that a large LM generates mostly coherent and consistent reflections.
Overall, the work of this thesis broadens access to NLP for MI significantly as well as presents a wide range of findings on related natural language understanding/generation tasks with a real-world focus. Thus, our contributions lay the groundwork for the broader NLP community to be more engaged in research for MI, which will ultimately improve the quality of life for recipients of MI counselling
