Interpreting Questions with a Log-Linear Ranking Model in a Virtual Patient Dialogue System
Code (0)
등록된 구현이 없습니다.
Tasks
Question AnsweringSemantic ParsingSimilar Papers 제목 키워드 기반
Using Paraphrasing and Memory-Augmented Models to Combat Data Sparsity in Question Interpretation with a Virtual Patient Dialogue System
When interpreting questions in a virtual patient dialogue system one must inevitably tackle the challenge of a long tail of relatively infrequently asked questions. To make progress on this challenge, we investigate the …
Data AugmentationGeneral ClassificationOne-Shot LearningAutomatic classification of doctor-patient questions for a virtual patient record query task
We present the work-in-progress of automating the classification of doctor-patient questions in the context of a simulated consultation with a virtual patient. We classify questions according to the computational strateg…
BIG-bench Machine LearningDialogue ManagementGeneral ClassificationInformation Retrieval+2Semantic Similarity To Improve Question Understanding in a Virtual Patient
In medicine, a communicating virtual patient or doctor allows students to train in medical diagnosis and develop skills to conduct a medical consultation. In this paper, we describe a conversational virtual standardized …
Medical DiagnosisSemantic SimilaritySemantic Textual SimilarityDialogue-Contextualized Re-ranking for Medical History-Taking
AI-driven medical history-taking is an important component in symptom checking, automated patient intake, triage, and other AI virtual care applications. As history-taking is extremely varied, machine learning models req…
Language ModelingLanguage ModellingRe-RankingSelf-Supervised Intermediate Fine-Tuning of Biomedical Language Models for Interpreting Patient Case Descriptions
Interpreting patient case descriptions has emerged as a challenging problem for biomedical NLP, where the aim is typically to predict diagnoses, to recommended treatments, or to answer questions about cases more generall…