Title: A Way out of the Odyssey: Analyzing and Combining Recent Insights for LSTMs
Authors: Shayne Longpre, Sabeek Pradhan, Caiming Xiong, Richard Socher
Keywords: Natural language processing, Deep learning, Supervised Learning
TL;DR: Relatively simple augmentations to the LSTM, such as Monte Carlo test time averaging, deep vector averaging, and residual connections, can yield massive accuracy improvements on text classification datasets.
Abstract:
LSTMs have become a basic building block for many deep NLP models. In recent years, many improvements and variations have been proposed for deep sequence models in general, and LSTMs in particular. We propose and analyze a series of architectural modifications for LSTM networks resulting in improved performance for text classification datasets. We observe compounding improvements on traditional LSTMs using Monte Carlo test-time model averaging, deep vector averaging (DVA), and residual connections, along with four other suggested modifications. Our analysis provides a simple, reliable, and high quality baseline model.
Venue: Submitted to ICLR 2017