34 citations · 114 across the 20 of their papers we have counts for
21 papers
An Automatic SOAP Classification System Using Weakly Supervision And Transfer Learning
Sunjae Kwon, Zhichao Yang, Hong Yu
In this paper, we introduce a comprehensive framework for developing a machine learning-based SOAP (Subjective, Objective, Assessment, and Plan) classification system without manua…
Knowledge Injected Prompt Based Fine-tuning for Multi-label Few-shot ICD Coding
Zhichao Yang, Shufan Wang, Bhanu Pratap Singh Rawat +2
Automatic International Classification of Diseases (ICD) coding aims to assign multiple ICD codes to a medical note with average length of 3,000+ tokens. This task is challenging d…
Advanced Conditional Variational Autoencoders (A-CVAE): Towards interpreting open-domain conversation generation via disentangling latent feature representation
Ye Wang, Jingbo Liao, Hong Yu +3
Currently end-to-end deep learning based open-domain dialogue systems remain black box models, making it easy to generate irrelevant contents with data-driven models. Specifically,…
Improving Formality Style Transfer with Context-Aware Rule Injection
Zonghai Yao, Hong Yu
Models pre-trained on large-scale regular text corpora often do not work well for user-generated data where the language styles differ significantly from the mainstream text. Here…
A Dual-Questioning Attention Network for Emotion-Cause Pair Extraction with Context Awareness
Qixuan Sun, Yaqi Yin, Hong Yu
Emotion-cause pair extraction (ECPE), an emerging task in sentiment analysis, aims at extracting pairs of emotions and their corresponding causes in documents. This is a more chall…
Dynamic Data Selection for Curriculum Learning via Ability Estimation
John P. Lalor, Hong Yu
Curriculum learning methods typically rely on heuristics to estimate the difficulty of training examples or the ability of the model. In this work, we propose replacing difficulty…
Birth-Burst in Evolving Networks
Dong Chen, Hong Yu
The evolution of complex networks is governed by both growing rules and internal properties. Most evolving network models (e.g. preferential attachment) emphasize on the growing st…
Calibrating Structured Output Predictors for Natural Language Processing
Abhyuday Jagannatha, Hong Yu
We address the problem of calibrating prediction confidence for output entities of interest in natural language processing (NLP) applications. It is important that NLP applications…
Neural Data-to-Text Generation with Dynamic Content Planning
Kai Chen, Fayuan Li, Baotian Hu +3
Neural data-to-text generation models have achieved significant advancement in recent years. However, these models have two shortcomings: the generated texts tend to miss some vita…
Continual Domain-Tuning for Pretrained Language Models
Subendhu Rongali, Abhyuday Jagannatha, Bhanu Pratap Singh Rawat +1
Pre-trained language models (LM) such as BERT, DistilBERT, and RoBERTa can be tuned for different domains (domain-tuning) by continuing the pre-training phase on a new target domai…
MetaMT,a MetaLearning Method Leveraging Multiple Domain Data for Low Resource Machine Translation
Rumeng Li, Xun Wang, Hong Yu
Manipulating training data leads to robust neural models for MT.
Learning Latent Parameters without Human Response Patterns: Item Response Theory with Artificial Crowds
John P. Lalor, Hao Wu, Hong Yu
Incorporating Item Response Theory (IRT) into NLP tasks can provide valuable information about model performance and behavior. Traditionally, IRT models are learned using human res…
Sentence Simplification with Memory-Augmented Neural Networks
Tu Vu, Baotian Hu, Tsendsuren Munkhdalai +1
Sentence simplification aims to simplify the content and structure of complex sentences, and thus make them easier to interpret for human readers, and easier to process for downstr…
Reinforcement Learning based QoS/QoE-aware Service Function Chaining in Software-Driven 5G Slices
Xi Chen, Zonghang Li, Yupeng Zhang +4
With the ever growing diversity of devices and applications that will be connected to 5G networks, flexible and agile service orchestration with acknowledged QoE that satisfies end…
Meta Networks
Tsendsuren Munkhdalai, Hong Yu
Neural networks have been successfully applied in applications with a large amount of labeled data. However, the task of rapid generalization on new concepts with small training da…
Soft Label Memorization-Generalization for Natural Language Inference
John P. Lalor, Hao Wu, Hong Yu
Often when multiple labels are obtained for a training example it is assumed that there is an element of noise that must be accounted for. It has been shown that this disagreement…
Understanding Deep Learning Performance through an Examination of Test Set Difficulty: A Psychometric Case Study
John P. Lalor, Hao Wu, Tsendsuren Munkhdalai +1
Interpreting the performance of deep learning models beyond test set accuracy is challenging. Characteristics of individual data points are often not considered during evaluation,…
Scalable Fine-grained Path Control in Software Defined Networks
Long Luo, Hongfang Yu, Shouxi Luo
The OpenFlow-based SDN is widely studied to better network performance through planning fine-grained paths. However, being designed to configure path hop-by-hop, it faces the scala…
Reasoning with Memory Augmented Neural Networks for Language Comprehension
Tsendsuren Munkhdalai, Hong Yu
Hypothesis testing is an important cognitive process that supports human reasoning. In this paper, we introduce a computational hypothesis testing approach based on memory augmente…
Structured prediction models for RNN based sequence labeling in clinical text
Abhyuday Jagannatha, Hong Yu
Sequence labeling is a widely used method for named entity recognition and information extraction from unstructured natural language data. In clinical domain one major application…
Building an Evaluation Scale using Item Response Theory
John P. Lalor, Hao Wu, Hong Yu
Evaluation of NLP methods requires testing against a previously vetted gold-standard test set and reporting standard metrics (accuracy/precision/recall/F1). The current assumption…