Oladimeji Farri

TL;DR: The authors proposed a stacked residual LSTM network for paraphrase generation, which adds residual connections between LSTMs layers for efficient training, and achieved state-of-the-art performance on three different datasets: PPDB, WikiAnswers and MSCOCO.

TL;DR: Investigation of cutting-edge deep learning methods for information extraction from medical imaging free text reports at a multi-institutional scale and compares them to the state-of-the-art domain-specific rule-based system - PEFinder and traditional machine learning methods - SVM and Adaboost suggests feasibility of broader usage of neural network models in automated classification of multi-Institutional imaging text reports.

TL;DR: This work builds several semi-supervised convolutional neural network models for ADE classification in tweets, specifically leveraging different types of unlabeled data in developing the models to address the problem.

TL;DR: A novel dependent reading bidirectional LSTM network (DR-BiLSTM) is proposed to efficiently model the relationship between a premise and a hypothesis during encoding and inference in the natural language inference (NLI) task.

TL;DR: This work is the first to explore deep learning models for paraphrase generation with a stacked residual LSTM network, where it adds residual connections between L STM layers for efficient training of deep LSTMs.