b'Hong Yu'

research

∙ 08/26/2022

Extracting Biomedical Factual Knowledge Using Pretrained Language Model and Electronic Health Record Context

Language Models (LMs) have performed well on biomedical natural language...

Zonghai Yao, et al. ∙

research

∙ 07/26/2022

Advanced Conditional Variational Autoencoders (A-CVAE): Towards interpreting open-domain conversation generation via disentangling latent feature representation

Currently end-to-end deep learning based open-domain dialogue systems re...

Ye Wang, et al. ∙

research

∙ 05/29/2022

Learning as Conversation: Dialogue Systems Reinforced for Information Acquisition

We propose novel AI-empowered chat bots for learning as conversation whe...

Pengshan Cai, et al. ∙

research

∙ 05/12/2022

ScAN: Suicide Attempt and Ideation Events Dataset

Suicide is an important public health concern and one of the leading cau...

Bhanu Pratap Singh Rawat, et al. ∙

research

∙ 04/18/2022

Caption Feature Space Regularization for Audio Captioning

Audio captioning aims at describing the content of audio clips with huma...

Yiming Zhang, et al. ∙

research

∙ 06/01/2021

Improving Formality Style Transfer with Context-Aware Rule Injection

Models pre-trained on large-scale regular text corpora often do not work...

Zonghai Yao, et al. ∙

research

∙ 05/20/2021

CREAD: Combined Resolution of Ellipses and Anaphora in Dialogues

Anaphora and ellipses are two common phenomena in dialogues. Without res...

Bo-Hsiang Tseng, et al. ∙

research

∙ 04/16/2021

Membership Inference Attack Susceptibility of Clinical Language Models

Deep Neural Network (DNN) models have been shown to have high empirical ...

Abhyuday Jagannatha, et al. ∙

research

∙ 04/15/2021

A Dual-Questioning Attention Network for Emotion-Cause Pair Extraction with Context Awareness

Emotion-cause pair extraction (ECPE), an emerging task in sentiment anal...

Qixuan Sun, et al. ∙

research

∙ 12/14/2020

A prognostic dynamic model applicable to infectious diseases providing easily visualized guides – A case study of COVID-19 in the UK

A reasonable prediction of infectious diseases transmission process unde...

Yuxuan Zhang, et al. ∙

research

∙ 10/30/2020

Dynamic Data Selection for Curriculum Learning via Ability Estimation

Curriculum learning methods typically rely on heuristics to estimate the...

John P. Lalor, et al. ∙

research

∙ 10/24/2020

Conversational Semantic Parsing for Dialog State Tracking

We consider a new perspective on dialog state tracking (DST), the task o...

Jianpeng Cheng, et al. ∙

research

∙ 04/16/2020

Neural Data-to-Text Generation with Dynamic Content Planning

Neural data-to-text generation models have achieved significant advancem...

Kai Chen, et al. ∙

research

∙ 04/09/2020

Calibrating Structured Output Predictors for Natural Language Processing

We address the problem of calibrating prediction confidence for output e...

Abhyuday Jagannatha, et al. ∙

research

∙ 04/05/2020

Improved Pretraining for Domain-specific Contextual Embedding Models

We investigate methods to mitigate catastrophic forgetting during domain...

Subendhu Rongali, et al. ∙

research

∙ 12/11/2019

MetaMT,a MetaLearning Method Leveraging Multiple Domain Data for Low Resource Machine Translation

Manipulating training data leads to robust neural models for MT....

Rumeng Li, et al. ∙

research

∙ 11/25/2019

ICD Coding from Clinical Text Using Multi-Filter Residual Convolutional Neural Network

Automated ICD coding, which assigns the International Classification of ...

Fei Li, et al. ∙

research

∙ 10/09/2019

Large-scale Gastric Cancer Screening and Localization Using Multi-task Deep Neural Network

Gastric cancer is one of the most common cancers, which ranks third amon...

Hong Yu, et al. ∙

research

∙ 08/31/2019

Generating Classical Chinese Poems from Vernacular Chinese

Classical Chinese poetry is a jewel in the treasure house of Chinese cul...

Zhichao Yang, et al. ∙

research

∙ 08/29/2019

Learning Latent Parameters without Human Response Patterns: Item Response Theory with Artificial Crowds

Incorporating Item Response Theory (IRT) into NLP tasks can provide valu...

John P. Lalor, et al. ∙

research

∙ 11/29/2018

HYPE: A High Performing NLP System for Automatically Detecting Hypoglycemia Events from Electronic Health Record Notes

Hypoglycemia is common and potentially dangerous among those treated for...

Yonghao Jin, et al. ∙

research

∙ 09/18/2018

Language Identification with Deep Bottleneck Features

In this paper we proposed an end-to-end short utterances speech language...

Zhanyu Ma, et al. ∙

research

∙ 08/02/2018

Deep Neural Network for Analysis of DNA Methylation Data

Many researches demonstrated that the DNA methylation, which occurs in t...

Hong Yu, et al. ∙

research

∙ 08/02/2018

Histogram Transform-based Speaker Identification

A novel text-independent speaker identification (SI) method is proposed....

Zhanyu Ma, et al. ∙

research

∙ 04/20/2018

Sentence Simplification with Memory-Augmented Neural Networks

Sentence simplification aims to simplify the content and structure of co...

Tu Vu, et al. ∙

research

∙ 03/02/2017

Meta Networks

Neural networks have been successfully applied in applications with a la...

Tsendsuren Munkhdalai, et al. ∙

research

∙ 03/01/2017

Unsupervised Ensemble Ranking of Terms in Electronic Health Record Notes Based on Their Importance to Patients

Background: Electronic health record (EHR) notes contain abundant medica...

Jinying Chen, et al. ∙

research

∙ 02/27/2017

CIFT: Crowd-Informed Fine-Tuning to Improve Machine Learning Ability

Item Response Theory (IRT) allows for measuring ability of Machine Learn...

John P. Lalor, et al. ∙

research

∙ 02/15/2017

An Analysis of Ability in Deep Neural Networks

Deep neural networks (DNNs) have made significant progress in a number o...

John P. Lalor, et al. ∙

research

∙ 11/14/2016

Ranking medical jargon in electronic health record notes by adapted distant supervision

Objective: Allowing patients to access their own electronic health recor...

Jinying Chen, et al. ∙

research

∙ 11/04/2016

Learning to Rank Scientific Documents from the Crowd

Finding related published articles is an important task in any science, ...

Jesse M Lingeman, et al. ∙

research

∙ 10/20/2016

Reasoning with Memory Augmented Neural Networks for Language Comprehension

Hypothesis testing is an important cognitive process that supports human...

Tsendsuren Munkhdalai, et al. ∙

research

∙ 08/01/2016

Structured prediction models for RNN based sequence labeling in clinical text

Sequence labeling is a widely used method for named entity recognition a...

Abhyuday Jagannatha, et al. ∙

research

∙ 07/15/2016

Neural Tree Indexers for Text Understanding

Recurrent neural networks (RNNs) process input text sequentially and mod...

Tsendsuren Munkhdalai, et al. ∙

research

∙ 07/14/2016

Neural Semantic Encoders

We present a memory augmented neural network for natural language unders...

Tsendsuren Munkhdalai, et al. ∙

research

∙ 06/26/2016

Learning for Biomedical Information Extraction: Methodological Review of Recent Advances

Biomedical information extraction (BioIE) is important to many applicati...

Feifan Liu, et al. ∙

research

∙ 06/25/2016

Bidirectional Recurrent Neural Networks for Medical Event Detection in Electronic Health Records

Sequence labeling for extraction of medical events and their attributes ...

Abhyuday Jagannatha, et al. ∙

research

∙ 05/28/2016

Building an Evaluation Scale using Item Response Theory

Evaluation of NLP methods requires testing against a previously vetted g...

John P. Lalor, et al. ∙

research

∙ 12/22/2015

Heuristic algorithms for finding distribution reducts in probabilistic rough set model

Attribute reduction is one of the most important topics in rough set the...

Xi'ao Ma, et al. ∙

Hong Yu

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