@inproceedings{zhang-etal-2018-sentence,
title = "Sentence-State {LSTM} for Text Representation",
author = "Zhang, Yue and
Liu, Qi and
Song, Linfeng",
editor = "Gurevych, Iryna and
Miyao, Yusuke",
booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jul,
year = "2018",
address = "Melbourne, Australia",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/P18-1030/",
doi = "10.18653/v1/P18-1030",
pages = "317--327",
abstract = "Bi-directional LSTMs are a powerful tool for text representation. On the other hand, they have been shown to suffer various limitations due to their sequential nature. We investigate an alternative LSTM structure for encoding text, which consists of a parallel state for each word. Recurrent steps are used to perform local and global information exchange between words simultaneously, rather than incremental reading of a sequence of words. Results on various classification and sequence labelling benchmarks show that the proposed model has strong representation power, giving highly competitive performances compared to stacked BiLSTM models with similar parameter numbers."
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%0 Conference Proceedings
%T Sentence-State LSTM for Text Representation
%A Zhang, Yue
%A Liu, Qi
%A Song, Linfeng
%Y Gurevych, Iryna
%Y Miyao, Yusuke
%S Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2018
%8 July
%I Association for Computational Linguistics
%C Melbourne, Australia
%F zhang-etal-2018-sentence
%X Bi-directional LSTMs are a powerful tool for text representation. On the other hand, they have been shown to suffer various limitations due to their sequential nature. We investigate an alternative LSTM structure for encoding text, which consists of a parallel state for each word. Recurrent steps are used to perform local and global information exchange between words simultaneously, rather than incremental reading of a sequence of words. Results on various classification and sequence labelling benchmarks show that the proposed model has strong representation power, giving highly competitive performances compared to stacked BiLSTM models with similar parameter numbers.
%R 10.18653/v1/P18-1030
%U https://aclanthology.org/P18-1030/
%U https://doi.org/10.18653/v1/P18-1030
%P 317-327
Markdown (Informal)
[Sentence-State LSTM for Text Representation](https://aclanthology.org/P18-1030/) (Zhang et al., ACL 2018)
ACL
- Yue Zhang, Qi Liu, and Linfeng Song. 2018. Sentence-State LSTM for Text Representation. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 317–327, Melbourne, Australia. Association for Computational Linguistics.