@inproceedings{oh-etal-2019-topic,
title = "Topic-Guided Coherence Modeling for Sentence Ordering by Preserving Global and Local Information",
author = "Oh, Byungkook and
Seo, Seungmin and
Shin, Cheolheon and
Jo, Eunju and
Lee, Kyong-Ho",
editor = "Inui, Kentaro and
Jiang, Jing and
Ng, Vincent and
Wan, Xiaojun",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
month = nov,
year = "2019",
address = "Hong Kong, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D19-1232",
doi = "10.18653/v1/D19-1232",
pages = "2273--2283",
abstract = "We propose a novel topic-guided coherence modeling (TGCM) for sentence ordering. Our attention based pointer decoder directly utilize sentence vectors in a permutation-invariant manner, without being compressed into a single fixed-length vector as the paragraph representation. Thus, TGCM can improve global dependencies among sentences and preserve relatively informative paragraph-level semantics. Moreover, to predict the next sentence, we capture topic-enhanced sentence-pair interactions between the current predicted sentence and each next-sentence candidate. With the coherent topical context matching, we promote local dependencies that help identify the tight semantic connections for sentence ordering. The experimental results show that TGCM outperforms state-of-the-art models from various perspectives.",
}
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%0 Conference Proceedings
%T Topic-Guided Coherence Modeling for Sentence Ordering by Preserving Global and Local Information
%A Oh, Byungkook
%A Seo, Seungmin
%A Shin, Cheolheon
%A Jo, Eunju
%A Lee, Kyong-Ho
%Y Inui, Kentaro
%Y Jiang, Jing
%Y Ng, Vincent
%Y Wan, Xiaojun
%S Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
%D 2019
%8 November
%I Association for Computational Linguistics
%C Hong Kong, China
%F oh-etal-2019-topic
%X We propose a novel topic-guided coherence modeling (TGCM) for sentence ordering. Our attention based pointer decoder directly utilize sentence vectors in a permutation-invariant manner, without being compressed into a single fixed-length vector as the paragraph representation. Thus, TGCM can improve global dependencies among sentences and preserve relatively informative paragraph-level semantics. Moreover, to predict the next sentence, we capture topic-enhanced sentence-pair interactions between the current predicted sentence and each next-sentence candidate. With the coherent topical context matching, we promote local dependencies that help identify the tight semantic connections for sentence ordering. The experimental results show that TGCM outperforms state-of-the-art models from various perspectives.
%R 10.18653/v1/D19-1232
%U https://aclanthology.org/D19-1232
%U https://doi.org/10.18653/v1/D19-1232
%P 2273-2283
Markdown (Informal)
[Topic-Guided Coherence Modeling for Sentence Ordering by Preserving Global and Local Information](https://aclanthology.org/D19-1232) (Oh et al., EMNLP-IJCNLP 2019)
ACL