1 code implementation • 17 Nov 2021 • Rhys Compton, Ilya Valmianski, Li Deng, Costa Huang, Namit Katariya, Xavier Amatriain, Anitha Kannan
We present MEDCOD, a Medically-Accurate, Emotive, Diverse, and Controllable Dialog system with a unique approach to the natural language generator module.
1 code implementation • 15 Nov 2021 • Varun Nair, Namit Katariya, Xavier Amatriain, Ilya Valmianski, Anitha Kannan
Summarized conversations are used to facilitate patient hand-offs between physicians, and as part of providing care in the future.
no code implementations • NAACL (NLPMC) 2021 • Bharath Chintagunta, Namit Katariya, Xavier Amatriain, Anitha Kannan
In medical dialogue summarization, summaries must be coherent and must capture all the medically relevant information in the dialogue.
no code implementations • Findings of the Association for Computational Linguistics 2020 • Anirudh Joshi, Namit Katariya, Xavier Amatriain, Anitha Kannan
Understanding a medical conversation between a patient and a physician poses unique natural language understanding challenge since it combines elements of standard open-ended conversation with very domain-specific elements that require expertise and medical knowledge.
no code implementations • 18 Sep 2020 • Anirudh Joshi, Namit Katariya, Xavier Amatriain, Anitha Kannan
Understanding a medical conversation between a patient and a physician poses a unique natural language understanding challenge since it combines elements of standard open ended conversation with very domain specific elements that require expertise and medical knowledge.
no code implementations • 4 Aug 2020 • Clara H. McCreery, Namit Katariya, Anitha Kannan, Manish Chablani, Xavier Amatriain
People increasingly search online for answers to their medical questions but the rate at which medical questions are asked online significantly exceeds the capacity of qualified people to answer them.
no code implementations • 16 Nov 2019 • Sam Shleifer, Manish Chablani, Anitha Kannan, Namit Katariya, Xavier Amatriain
Generative seq2seq dialogue systems are trained to predict the next word in dialogues that have already occurred.
no code implementations • 9 Oct 2019 • Clara McCreery, Namit Katariya, Anitha Kannan, Manish Chablani, Xavier Amatriain
The rate at which medical questions are asked online far exceeds the capacity of qualified people to answer them, and many of these questions are not unique.
no code implementations • 7 Oct 2019 • Viraj Prabhu, Anitha Kannan, Geoffrey J. Tso, Namit Katariya, Manish Chablani, David Sontag, Xavier Amatriain
Machine-learned diagnosis models have shown promise as medical aides but are trained under a closed-set assumption, i. e. that models will only encounter conditions on which they have been trained.
no code implementations • 4 Oct 2019 • Sam Shleifer, Manish Chablani, Namit Katariya, Anitha Kannan, Xavier Amatriain
Only 12% of our discriminative approach's responses are worse than the doctor's response in the same conversational context, compared to 18% for the generative model.