Towards Understanding Counseling Conversations: Domain Knowledge and Large Language Models
Conference of the European Chapter of the Association for Computational Linguistics(2024)
Abstract
Understanding the dynamics of counseling conversations is an important task,
yet it is a challenging NLP problem regardless of the recent advance of
Transformer-based pre-trained language models. This paper proposes a systematic
approach to examine the efficacy of domain knowledge and large language models
(LLMs) in better representing conversations between a crisis counselor and a
help seeker. We empirically show that state-of-the-art language models such as
Transformer-based models and GPT models fail to predict the conversation
outcome. To provide richer context to conversations, we incorporate
human-annotated domain knowledge and LLM-generated features; simple integration
of domain knowledge and LLM features improves the model performance by
approximately 15
features can be exploited to better characterize counseling conversations when
they are used as an additional context to conversations.
MoreTranslated text
AI Read Science
Must-Reading Tree
Example
Generate MRT to find the research sequence of this paper
Chat Paper
Summary is being generated by the instructions you defined