Search Results for author:
Found 8 papers, 2 papers with code
Keyword Augmentation via Generative Methods
In this paper, we propose a keyword augmentation method based on generative seq2seq model and trie-based search mechanism, which is able to generate high-quality keywords for any products or product lists.
Imitator Learning: Achieve Out-of-the-Box Imitation Ability in Variable Environments
In this work, we focus on imitator learning based on only one expert demonstration.
A Data-Centric Framework for Composable NLP Workflows
Empirical natural language processing (NLP) systems in application domains (e. g., healthcare, finance, education) involve interoperation among multiple components, ranging from data ingestion, human annotation, to text retrieval, analysis, generation, and visualization.
Learning Hierarchical Representations of Electronic Health Records for Clinical Outcome Prediction
Our model learns hierarchical representationsof event sequences, to adaptively distinguish between short-range and long-range events, and accurately capture coretemporal dependencies.
Differentiable Expected BLEU for Text Generation
Neural text generation models such as recurrent networks are typically trained by maximizing data log-likelihood based on cross entropy.
Texar: A Modularized, Versatile, and Extensible Toolkit for Text Generation
The versatile toolkit also fosters technique sharing across different text generation tasks.
Texar: A Modularized, Versatile, and Extensible Toolbox for Text Generation
The features make Texar particularly suitable for technique sharing and generalization across different text generation applications.
Towards Automated ICD Coding Using Deep Learning
Considering the complicated and dedicated process to assign correct codes to each patient admission based on overall diagnosis, we propose a hierarchical deep learning model with attention mechanism which can automatically assign ICD diagnostic codes given written diagnosis.
