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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Hits 1 – 7 of 7 1 Entity-Enriched Neural Models for Clinical Question Answering Rawat, Bhanu Pratap Singh; Weng, Wei-Hung; Min, So Yeon... In: arXiv (2021) BASE Show details 2 What Disease Does This Patient Have? A Large-Scale Open Domain Question Answering Dataset from Medical Exams Jin, Di; Pan, Eileen; Oufattole, Nassim... In: Multidisciplinary Digital Publishing Institute (2021) BASE Show details 3 What Disease Does This Patient Have? A Large-Scale Open Domain Question Answering Dataset from Medical Exams Jin, Di; Pan, Eileen; Oufattole, Nassim; Weng, Wei-Hung; Fang, Hanyi; Szolovits, Peter In: Multidisciplinary Digital Publishing Institute (2021) Abstract: Open domain question answering (OpenQA) tasks have been recently attracting more and more attention from the natural language processing (NLP) community. In this work, we present the first free-form multiple-choice OpenQA dataset for solving medical problems, MedQA , collected from the professional medical board exams. It covers three languages: English, simplified Chinese, and traditional Chinese, and contains 12,723, 34,251, and 14,123 questions for the three languages, respectively. We implement both rule-based and popular neural methods by sequentially combining a document retriever and a machine comprehension model. Through experiments, we find that even the current best method can only achieve 36.7%, 42.0%, and 70.1% of test accuracy on the English, traditional Chinese, and simplified Chinese questions, respectively. We expect MedQA to present great challenges to existing OpenQA systems and hope that it can serve as a platform to promote much stronger OpenQA models from the NLP community in the future. URL: https://hdl.handle.net/1721.1/136684.2 BASE Hide details 4 Unsupervised Clinical Language Translation Weng, Wei-Hung; Szolovits, Peter; Chung, Yu-An In: arXiv (2021) BASE Show details 5 Clinical Text Summarization with Syntax-Based Negation and Semantic Concept Identification ... Weng, Wei-Hung; Chung, Yu-An; Tong, Schrasing. - : arXiv, 2020 BASE Show details 6 Unsupervised Clinical Language Translation ... Weng, Wei-Hung; Chung, Yu-An; Szolovits, Peter. - : arXiv, 2019 BASE Show details 7 Towards Unsupervised Speech-to-Text Translation ... Chung, Yu-An; Weng, Wei-Hung; Tong, Schrasing. - : arXiv, 2018 BASE Show details

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