Evaluating Embeddings for One-Shot Classification of Doctor-AI Consultations
CoRR(2024)
摘要
Effective communication between healthcare providers and patients is crucial
to providing high-quality patient care. In this work, we investigate how
Doctor-written and AI-generated texts in healthcare consultations can be
classified using state-of-the-art embeddings and one-shot classification
systems. By analyzing embeddings such as bag-of-words, character n-grams,
Word2Vec, GloVe, fastText, and GPT2 embeddings, we examine how well our
one-shot classification systems capture semantic information within medical
consultations. Results show that the embeddings are capable of capturing
semantic features from text in a reliable and adaptable manner. Overall,
Word2Vec, GloVe and Character n-grams embeddings performed well, indicating
their suitability for modeling targeted to this task. GPT2 embedding also shows
notable performance, indicating its suitability for models tailored to this
task as well. Our machine learning architectures significantly improved the
quality of health conversations when training data are scarce, improving
communication between patients and healthcare providers.
更多查看译文
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要