@inproceedings{cui-etal-2018-deep,
title = "Deep Attentive Sentence Ordering Network",
author = "Cui, Baiyun and
Li, Yingming and
Chen, Ming and
Zhang, Zhongfei",
editor = "Riloff, Ellen and
Chiang, David and
Hockenmaier, Julia and
Tsujii, Jun{'}ichi",
booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
month = oct # "-" # nov,
year = "2018",
address = "Brussels, Belgium",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D18-1465",
doi = "10.18653/v1/D18-1465",
pages = "4340--4349",
abstract = "In this paper, we propose a novel deep attentive sentence ordering network (referred as ATTOrderNet) which integrates self-attention mechanism with LSTMs in the encoding of input sentences. It enables us to capture global dependencies among sentences regardless of their input order and obtains a reliable representation of the sentence set. With this representation, a pointer network is exploited to generate an ordered sequence. The proposed model is evaluated on Sentence Ordering and Order Discrimination tasks. The extensive experimental results demonstrate its effectiveness and superiority to the state-of-the-art methods.",
}
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<abstract>In this paper, we propose a novel deep attentive sentence ordering network (referred as ATTOrderNet) which integrates self-attention mechanism with LSTMs in the encoding of input sentences. It enables us to capture global dependencies among sentences regardless of their input order and obtains a reliable representation of the sentence set. With this representation, a pointer network is exploited to generate an ordered sequence. The proposed model is evaluated on Sentence Ordering and Order Discrimination tasks. The extensive experimental results demonstrate its effectiveness and superiority to the state-of-the-art methods.</abstract>
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%0 Conference Proceedings
%T Deep Attentive Sentence Ordering Network
%A Cui, Baiyun
%A Li, Yingming
%A Chen, Ming
%A Zhang, Zhongfei
%Y Riloff, Ellen
%Y Chiang, David
%Y Hockenmaier, Julia
%Y Tsujii, Jun’ichi
%S Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
%D 2018
%8 oct nov
%I Association for Computational Linguistics
%C Brussels, Belgium
%F cui-etal-2018-deep
%X In this paper, we propose a novel deep attentive sentence ordering network (referred as ATTOrderNet) which integrates self-attention mechanism with LSTMs in the encoding of input sentences. It enables us to capture global dependencies among sentences regardless of their input order and obtains a reliable representation of the sentence set. With this representation, a pointer network is exploited to generate an ordered sequence. The proposed model is evaluated on Sentence Ordering and Order Discrimination tasks. The extensive experimental results demonstrate its effectiveness and superiority to the state-of-the-art methods.
%R 10.18653/v1/D18-1465
%U https://aclanthology.org/D18-1465
%U https://doi.org/10.18653/v1/D18-1465
%P 4340-4349
Markdown (Informal)
[Deep Attentive Sentence Ordering Network](https://aclanthology.org/D18-1465) (Cui et al., EMNLP 2018)
ACL
- Baiyun Cui, Yingming Li, Ming Chen, and Zhongfei Zhang. 2018. Deep Attentive Sentence Ordering Network. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 4340–4349, Brussels, Belgium. Association for Computational Linguistics.