James Henderson

Incremental Recurrent Neural Network Dependency Parser with Search-based Discriminative Training

2015

We propose a discriminatively trained recurrent neural network (RNN) that predicts the actions for a fast and accurate shift-reduce dependency parser. The RNN uses its output-dependent model structure to compute hidden vectors that encode the …

Plug and Play Autoencoders for Conditional Text Generation

2020

Text autoencoders are commonly used for conditional generation tasks such as style transfer.We propose methods which are plug and play, where any pretrained autoencoder can be used, and only require learning a mapping within the …

Privacy-Preserving Natural Language Processing for Clinical Notes

2025 · Preprints.org

The increasing adoption of Natural Language Processing (NLP) in healthcare has the potential to transform clinical practices by enabling the efficient extraction of insights from unstructured clinical notes. However, the sensitive nature of patient information …

Document-Level Neural Machine Translation with Hierarchical Attention Networks

2018

Neural Machine Translation (NMT) can be improved by including document-level contextual information. For this purpose, we propose a hierarchical attention model to capture the context in a structured and dynamic manner. The model is integrated …

GILE: A Generalized Input-Label Embedding for Text Classification

2019 · Transactions of the Association for Computational Linguistics

Neural text classification models typically treat output labels as categorical variables that lack description and semantics. This forces their parametrization to be dependent on the label set size, and, hence, they are unable to scale …