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%. We argue that both domain knowledge and LLM-generated features can be exploited to better characterize counseling conversations when they are used as an additional context to conversations.- Anthology ID:
- 2024.findings-eacl.137
- Volume:
- Findings of the Association for Computational Linguistics: EACL 2024
- Month:
- March
- Year:
- 2024
- Address:
- St. Julian’s, Malta
- Editors:
- Yvette Graham, Matthew Purver
- Venue:
- Findings
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 2032–2047
- Language:
- URL:
- https://aclanthology.org/2024.findings-eacl.137
- DOI:
- Cite (ACL):
- Younghun Lee, Dan Goldwasser, and Laura Schwab Reese. 2024. Towards Understanding Counseling Conversations: Domain Knowledge and Large Language Models. In Findings of the Association for Computational Linguistics: EACL 2024, pages 2032–2047, St. Julian’s, Malta. Association for Computational Linguistics.
- Cite (Informal):
- Towards Understanding Counseling Conversations: Domain Knowledge and Large Language Models (Lee et al., Findings 2024)
- PDF:
- https://preview.aclanthology.org/proper-vol2-ingestion/2024.findings-eacl.137.pdf