33 citations · 70 across the 11 of their papers we have counts for
10 papers
Word-level confidence estimation for RNN transducers
Mingqiu Wang, Hagen Soltau, Laurent El Shafey +1
Confidence estimate is an often requested feature in applications such as medical transcription where errors can impact patient care and the confidence estimate could be used to al…
R2D2: Relational Text Decoding with Transformers
Aryan Arbabi, Mingqiu Wang, Laurent El Shafey +2
We propose a novel framework for modeling the interaction between graphical structures and the natural language text associated with their nodes and edges. Existing approaches typi…
Understanding Medical Conversations: Rich Transcription, Confidence Scores & Information Extraction
Hagen Soltau, Mingqiu Wang, Izhak Shafran +1
In this paper, we describe novel components for extracting clinically relevant information from medical conversations which will be available as Google APIs. We describe a transfor…
Google COVID-19 Search Trends Symptoms Dataset: Anonymization Process Description (version 1.0)
Shailesh Bavadekar, Andrew Dai, John Davis +27
This report describes the aggregation and anonymization process applied to the initial version of COVID-19 Search Trends symptoms dataset (published at https://goo.gle/covid19sympt…
The Medical Scribe: Corpus Development and Model Performance Analyses
Izhak Shafran, Nan Du, Linh Tran +11
There is a growing interest in creating tools to assist in clinical note generation using the audio of provider-patient encounters. Motivated by this goal and with the help of prov…
Learning to Infer Entities, Properties and their Relations from Clinical Conversations
Nan Du, Mingqiu Wang, Linh Tran +2
Recently we proposed the Span Attribute Tagging (SAT) Model (Du et al., 2019) to infer clinical entities (e.g., symptoms) and their properties (e.g., duration). It tackles the chal…