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- W2890744983 abstract "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." @default.
- W2890744983 created "2018-09-27" @default.
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- W2890744983 date "2018-01-01" @default.
- W2890744983 modified "2023-09-26" @default.
- W2890744983 title "Speed Reading: Learning to Read ForBackward via Shuttle" @default.
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- W2890744983 doi "https://doi.org/10.18653/v1/d18-1474" @default.
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