732 citations · 1.3k across the 22 of their papers we have counts for
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What Disease does this Patient Have? A Large-scale Open Domain Question Answering Dataset from Medical Exams
Di Jin, Eileen Pan, Nassim Oufattole +3
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 t…
Clinical Text Summarization with Syntax-Based Negation and Semantic Concept Identification
Wei-Hung Weng, Yu-An Chung, Schrasing Tong
In the era of clinical information explosion, a good strategy for clinical text summarization is helpful to improve the clinical workflow. The ideal summarization strategy can pres…
Human-centric Metric for Accelerating Pathology Reports Annotation
Ruibin Ma, Po-Hsuan Cameron Chen, Gang Li +4
Pathology reports contain useful information such as the main involved organ, diagnosis, etc. These information can be identified from the free text reports and used for large-scal…
Publicly Available Clinical BERT Embeddings
Emily Alsentzer, John R. Murphy, Willie Boag +4
Contextual word embedding models such as ELMo (Peters et al., 2018) and BERT (Devlin et al., 2018) have dramatically improved performance for many natural language processing (NLP)…
Unsupervised Clinical Language Translation
Wei-Hung Weng, Yu-An Chung, Peter Szolovits
As patients' access to their doctors' clinical notes becomes common, translating professional, clinical jargon to layperson-understandable language is essential to improve patient-…
Towards Unsupervised Speech-to-Text Translation
Yu-An Chung, Wei-Hung Weng, Schrasing Tong +1
We present a framework for building speech-to-text translation (ST) systems using only monolingual speech and text corpora, in other words, speech utterances from a source language…
Unsupervised Cross-Modal Alignment of Speech and Text Embedding Spaces
Yu-An Chung, Wei-Hung Weng, Schrasing Tong +1
Recent research has shown that word embedding spaces learned from text corpora of different languages can be aligned without any parallel data supervision. Inspired by the success…