Speed Reading: Learning to Read ForBackward via Shuttle
Tsu-Jui Fu, Wei-Yun Ma · 2018
We present LSTM-Shuttle, which applies human speed reading techniques to natural language processing tasks for accurate and efficient comprehension.In contrast to previous work, LSTM-Shuttle not only reads shuttling forward but also goes back.Shuttling forward enables high efficiency, and going backward gives the model a chance to recover lost information, ensuring better prediction.We evaluate LSTM-Shuttle on sentiment analysis, news classification, and cloze on IMDB, Rotten Tomatoes, AG, and Children's Book Test datasets.We show that LSTM-Shuttle predicts both better and more quickly.To demonstrate how LSTM-Shuttle actually behaves, we also analyze the shuttling operation and present a case study.