6 citations · 8 across the 7 of their papers we have counts for
7 papers
Injecting knowledge into language generation: a case study in auto-charting after-visit care instructions from medical dialogue
Maksim Eremeev, Ilya Valmianski, Xavier Amatriain +1
Factual correctness is often the limiting factor in practical applications of natural language generation in high-stakes domains such as healthcare. An essential requirement for ma…
Learning functional sections in medical conversations: iterative pseudo-labeling and human-in-the-loop approach
Mengqian Wang, Ilya Valmianski, Xavier Amatriain +1
Medical conversations between patients and medical professionals have implicit functional sections, such as "history taking", "summarization", "education", and "care plan." In this…
OSLAT: Open Set Label Attention Transformer for Medical Entity Retrieval and Span Extraction
Raymond Li, Ilya Valmianski, Li Deng +2
Medical entity span extraction and linking are critical steps for many healthcare NLP tasks. Most existing entity extraction methods either have a fixed vocabulary of medical entit…
MEDCOD: A Medically-Accurate, Emotive, Diverse, and Controllable Dialog System
Rhys Compton, Ilya Valmianski, Li Deng +4
We present MEDCOD, a Medically-Accurate, Emotive, Diverse, and Controllable Dialog system with a unique approach to the natural language generator module. MEDCOD has been developed…
Medically Aware GPT-3 as a Data Generator for Medical Dialogue Summarization
Bharath Chintagunta, Namit Katariya, Xavier Amatriain +1
In medical dialogue summarization, summaries must be coherent and must capture all the medically relevant information in the dialogue. However, learning effective models for summar…
Medical symptom recognition from patient text: An active learning approach for long-tailed multilabel distributions
Ali Mottaghi, Prathusha K Sarma, Xavier Amatriain +2
We study the problem of medical symptoms recognition from patient text, for the purposes of gathering pertinent information from the patient (known as history-taking). A typical pa…
Classification As Decoder: Trading Flexibility For Control In Neural Dialogue
Sam Shleifer, Manish Chablani, Namit Katariya +2
Generative seq2seq dialogue systems are trained to predict the next word in dialogues that have already occurred. They can learn from large unlabeled conversation datasets, build a…