Dhruvesh Patel


2020

pdf bib
Weakly Supervised Medication Regimen Extraction from Medical Conversations
Dhruvesh Patel | Sandeep Konam | Sai Prabhakar
Proceedings of the 3rd Clinical Natural Language Processing Workshop

Automated Medication Regimen (MR) extraction from medical conversations can not only improve recall and help patients follow through with their care plan, but also reduce the documentation burden for doctors. In this paper, we focus on extracting spans for frequency, route and change, corresponding to medications discussed in the conversation. We first describe a unique dataset of annotated doctor-patient conversations and then present a weakly supervised model architecture that can perform span extraction using noisy classification data. The model utilizes an attention bottleneck inside a classification model to perform the extraction. We experiment with several variants of attention scoring and projection functions and propose a novel transformer-based attention scoring function (TAScore). The proposed combination of TAScore and Fusedmax projection achieves a 10 point increase in Longest Common Substring F1 compared to the baseline of additive scoring plus softmax projection.

pdf bib
Reading Comprehension as Natural Language Inference:A Semantic Analysis
Anshuman Mishra | Dhruvesh Patel | Aparna Vijayakumar | Xiang Li | Pavan Kapanipathi | Kartik Talamadupula
Proceedings of the Ninth Joint Conference on Lexical and Computational Semantics

In the recent past, Natural language Inference (NLI) has gained significant attention, particularly given its promise for downstream NLP tasks. However, its true impact is limited and has not been well studied. Therefore, in this paper, we explore the utility of NLI for one of the most prominent downstream tasks, viz. Question Answering (QA). We transform one of the largest available MRC dataset (RACE) to an NLI form, and compare the performances of a state-of-the-art model (RoBERTa) on both these forms. We propose new characterizations of questions, and evaluate the performance of QA and NLI models on these categories. We highlight clear categories for which the model is able to perform better when the data is presented in a coherent entailment form, and a structured question-answer concatenation form, respectively.