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.- Anthology ID:
- D18-1474
- Volume:
- Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
- Month:
- October-November
- Year:
- 2018
- Address:
- Brussels, Belgium
- Editors:
- Ellen Riloff, David Chiang, Julia Hockenmaier, Jun’ichi Tsujii
- Venue:
- EMNLP
- SIG:
- SIGDAT
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 4439–4448
- Language:
- URL:
- https://preview.aclanthology.org/build-pipeline-with-new-library/D18-1474/
- DOI:
- 10.18653/v1/D18-1474
- Cite (ACL):
- Tsu-Jui Fu and Wei-Yun Ma. 2018. Speed Reading: Learning to Read ForBackward via Shuttle. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 4439–4448, Brussels, Belgium. Association for Computational Linguistics.
- Cite (Informal):
- Speed Reading: Learning to Read ForBackward via Shuttle (Fu & Ma, EMNLP 2018)
- PDF:
- https://preview.aclanthology.org/build-pipeline-with-new-library/D18-1474.pdf
- Code
- tsujuifu/pytorch_lstm-shuttle
- Data
- CBT, Children's Book Test, IMDb Movie Reviews