  This paper describes novel models tailored for a new application, that of
extracting the symptoms mentioned in clinical conversations along with their
status. Lack of any publicly available corpus in this privacy-sensitive domain
led us to develop our own corpus, consisting of about 3K conversations
annotated by professional medical scribes. We propose two novel deep learning
approaches to infer the symptom names and their status: (1) a new hierarchical
span-attribute tagging (\SAT) model, trained using curriculum learning, and (2)
a variant of sequence-to-sequence model which decodes the symptoms and their
status from a few speaker turns within a sliding window over the conversation.
This task stems from a realistic application of assisting medical providers in
capturing symptoms mentioned by patients from their clinical conversations. To
reflect this application, we define multiple metrics. From inter-rater
agreement, we find that the task is inherently difficult. We conduct
comprehensive evaluations on several contrasting conditions and observe that
the performance of the models range from an F-score of 0.5 to 0.8 depending on
the condition. Our analysis not only reveals the inherent challenges of the
task, but also provides useful directions to improve the models.
