Yonghui Wu

Clinical Named Entity Recognition Using Deep Learning Models.

2017 · PubMed

Clinical Named Entity Recognition (NER) is a critical natural language processing (NLP) task to extract important concepts (named entities) from clinical narratives. Researchers have extensively investigated machine learning models for clinical NER. Recently, there have …

Leveraging Monolingual Data with Self-Supervision for Multilingual Neural Machine Translation

2020 · arXiv (Cornell University)

Over the last few years two promising research directions in low-resource neural machine translation (NMT) have emerged. The first focuses on utilizing high-resource languages to improve the quality of low-resource languages via multilingual NMT. The …

Extracting Family History of Patients From Clinical Narratives: Exploring an End-to-End Solution With Deep Learning Models

2020 · JMIR Medical Informatics

BACKGROUND: Patients' family history (FH) is a critical risk factor associated with numerous diseases. However, FH information is not well captured in the structured database but often documented in clinical narratives. Natural language processing (NLP) …

Clinical concept and relation extraction using prompt-based machine reading comprehension

2023 · Journal of the American Medical Informatics Association

OBJECTIVE: To develop a natural language processing system that solves both clinical concept extraction and relation extraction in a unified prompt-based machine reading comprehension (MRC) architecture with good generalizability for cross-institution applications. METHODS: We formulate …

AutoRADP: An Interpretable Deep Learning Framework to Predict Rapid Progression for Alzheimer’s Disease and Related Dementias Using Electronic Health Records

2025 · medRxiv

Alzheimer's disease (AD) and AD-related dementias (ADRD) exhibit heterogeneous progression rates, with rapid progression (RP) posing significant challenges for timely intervention and treatment. The increasingly available patient-centered electronic health records (EHRs) have made it possible …

Clinical Abbreviation Disambiguation Using Neural Word Embeddings

2015

This study examined the use of neural word embeddings for clinical abbreviation disambiguation, a special case of word sense disambiguation (WSD). We investigated three different methods for deriving word embeddings from a large unlabeled clinical …

Exploring the Limits of Language Modeling

2016 · arXiv (Cornell University)

In this work we explore recent advances in Recurrent Neural Networks for large scale Language Modeling, a task central to language understanding. We extend current models to deal with two key challenges present in this …

Reward Augmented Maximum Likelihood for Neural Structured Prediction

2016 · arXiv (Cornell University)

A key problem in structured output prediction is direct optimization of the task reward function that matters for test evaluation. This paper presents a simple and computationally efficient approach to incorporate task reward into a …

Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation

2016 · arXiv (Cornell University)

Neural Machine Translation (NMT) is an end-to-end learning approach for automated translation, with the potential to overcome many of the weaknesses of conventional phrase-based translation systems. Unfortunately, NMT systems are known to be computationally expensive …

Google’s Multilingual Neural Machine Translation System: Enabling Zero-Shot Translation

2017 · Transactions of the Association for Computational Linguistics

We propose a simple solution to use a single Neural Machine Translation (NMT) model to translate between multiple languages. Our solution requires no changes to the model architecture from a standard NMT system but instead …

Sequence-to-Sequence Models Can Directly Translate Foreign Speech

2017

We present a recurrent encoder-decoder deep neural network architecture that directly translates speech in one language into text in another.The model does not explicitly transcribe the speech into text in the source language, nor does …

CLAMP – a toolkit for efficiently building customized clinical natural language processing pipelines

2017 · Journal of the American Medical Informatics Association

Existing general clinical natural language processing (NLP) systems such as MetaMap and Clinical Text Analysis and Knowledge Extraction System have been successfully applied to information extraction from clinical text. However, end users often have to …

Natural TTS Synthesis by Conditioning WaveNet on Mel Spectrogram Predictions

2017 · arXiv (Cornell University)

This paper describes Tacotron 2, a neural network architecture for speech synthesis directly from text. The system is composed of a recurrent sequence-to-sequence feature prediction network that maps character embeddings to mel-scale spectrograms, followed by …

Lingvo: a Modular and Scalable Framework for Sequence-to-Sequence Modeling

2019 · arXiv (Cornell University)

Lingvo is a Tensorflow framework offering a complete solution for collaborative deep learning research, with a particular focus towards sequence-to-sequence models. Lingvo models are composed of modular building blocks that are flexible and easily extensible, …

Gmail Smart Compose

2019

In this paper, we present Smart Compose, a novel system for generating interactive, real-time suggestions in Gmail that assists users in writing mails by reducing repetitive typing. In the design and deployment of such a …

Google's Multilingual Neural Machine Translation System: Enabling Zero-Shot Translation

2016 · arXiv (Cornell University)

We propose a simple solution to use a single Neural Machine Translation (NMT) model to translate between multiple languages. Our solution requires no change in the model architecture from our base system but instead introduces …

Massively Multilingual Neural Machine Translation in the Wild: Findings and Challenges

2019 · arXiv (Cornell University)

We introduce our efforts towards building a universal neural machine translation (NMT) system capable of translating between any language pair. We set a milestone towards this goal by building a single massively multilingual NMT model …

Tacotron: Towards End-to-End Speech Synthesis

2017

A text-to-speech synthesis system typically consists of multiple stages, such as a text analysis frontend, an acoustic model and an audio synthesis module.Building these components often requires extensive domain expertise and may contain brittle design …

Bytes Are All You Need: End-to-end Multilingual Speech Recognition and Synthesis with Bytes

2019

We present two end-to-end models: Audio-to-Byte (A2B) and Byte-to-Audio (B2A), for multilingual speech recognition and synthesis. Prior work has predominantly used characters, sub-words or words as the unit of choice to model text. These units …

Leveraging Weakly Supervised Data to Improve End-to-end Speech-to-text Translation

2019

End-to-end Speech Translation (ST) models have many potential advantages when compared to the cascade of Automatic Speech Recognition (ASR) and text Machine Translation (MT) models, including lowered inference latency and the avoidance of error compounding. …

Natural TTS Synthesis by Conditioning Wavenet on MEL Spectrogram Predictions

2018

This paper describes Tacotron 2, a neural network architecture for speech synthesis directly from text. The system is composed of a recurrent sequence-to-sequence feature prediction network that maps character embeddings to mel-scale spectrograms, followed by …

Learning to Speak Fluently in a Foreign Language: Multilingual Speech Synthesis and Cross-Language Voice Cloning

2019

We present a multispeaker, multilingual text-to-speech (TTS) synthesis model based on Tacotron that is able to produce high quality speech in multiple languages.Moreover, the model is able to transfer voices across languages, e.g.synthesize fluent Spanish …

Direct Speech-to-Speech Translation with a Sequence-to-Sequence Model

2019

We present an attention-based sequence-to-sequence neural network which can directly translate speech from one language into speech in another language, without relying on an intermediate text representation.The network is trained end-to-end, learning to map speech …

A large language model for electronic health records

2022 · npj Digital Medicine

There is an increasing interest in developing artificial intelligence (AI) systems to process and interpret electronic health records (EHRs). Natural language processing (NLP) powered by pretrained language models is the key technology for medical AI …