1. Sentiment Reasoning for Healthcare
- Author
-
Le-Duc, Khai, Nguyen, Khai-Nguyen, Tat, Bach Phan, Le, Duy, Ngo, Jerry, Vo-Dang, Long, Nguyen, Anh Totti, and Hy, Truong-Son
- Subjects
Computer Science - Computation and Language ,Computer Science - Artificial Intelligence ,Computer Science - Machine Learning ,Computer Science - Sound ,Electrical Engineering and Systems Science - Audio and Speech Processing - Abstract
Transparency in AI decision-making is crucial in healthcare due to the severe consequences of errors, and this is important for building trust among AI and users in sentiment analysis task. Incorporating reasoning capabilities helps Large Language Models (LLMs) understand human emotions within broader contexts, handle nuanced and ambiguous language, and infer underlying sentiments that may not be explicitly stated. In this work, we introduce a new task - Sentiment Reasoning - for both speech and text modalities, along with our proposed multimodal multitask framework and dataset. Our study showed that rationale-augmented training enhances model performance in sentiment classification across both human transcript and ASR settings. Also, we found that the generated rationales typically exhibit different vocabularies compared to human-generated rationales, but maintain similar semantics. All code, data (English-translated and Vietnamese) and models are published online: https://github.com/leduckhai/MultiMed, Comment: Preprint, 18 pages
- Published
- 2024