@inproceedings{xu-etal-2019-cross,
title = "A Cross-Domain Transferable Neural Coherence Model",
author = "Xu, Peng and
Saghir, Hamidreza and
Kang, Jin Sung and
Long, Teng and
Bose, Avishek Joey and
Cao, Yanshuai and
Cheung, Jackie Chi Kit",
editor = "Korhonen, Anna and
Traum, David and
M{\`a}rquez, Llu{\'\i}s",
booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
month = jul,
year = "2019",
address = "Florence, Italy",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/P19-1067",
doi = "10.18653/v1/P19-1067",
pages = "678--687",
abstract = "Coherence is an important aspect of text quality and is crucial for ensuring its readability. One important limitation of existing coherence models is that training on one domain does not easily generalize to unseen categories of text. Previous work advocates for generative models for cross-domain generalization, because for discriminative models, the space of incoherent sentence orderings to discriminate against during training is prohibitively large. In this work, we propose a local discriminative neural model with a much smaller negative sampling space that can efficiently learn against incorrect orderings. The proposed coherence model is simple in structure, yet it significantly outperforms previous state-of-art methods on a standard benchmark dataset on the Wall Street Journal corpus, as well as in multiple new challenging settings of transfer to unseen categories of discourse on Wikipedia articles.",
}
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<abstract>Coherence is an important aspect of text quality and is crucial for ensuring its readability. One important limitation of existing coherence models is that training on one domain does not easily generalize to unseen categories of text. Previous work advocates for generative models for cross-domain generalization, because for discriminative models, the space of incoherent sentence orderings to discriminate against during training is prohibitively large. In this work, we propose a local discriminative neural model with a much smaller negative sampling space that can efficiently learn against incorrect orderings. The proposed coherence model is simple in structure, yet it significantly outperforms previous state-of-art methods on a standard benchmark dataset on the Wall Street Journal corpus, as well as in multiple new challenging settings of transfer to unseen categories of discourse on Wikipedia articles.</abstract>
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%0 Conference Proceedings
%T A Cross-Domain Transferable Neural Coherence Model
%A Xu, Peng
%A Saghir, Hamidreza
%A Kang, Jin Sung
%A Long, Teng
%A Bose, Avishek Joey
%A Cao, Yanshuai
%A Cheung, Jackie Chi Kit
%Y Korhonen, Anna
%Y Traum, David
%Y Màrquez, Lluís
%S Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
%D 2019
%8 July
%I Association for Computational Linguistics
%C Florence, Italy
%F xu-etal-2019-cross
%X Coherence is an important aspect of text quality and is crucial for ensuring its readability. One important limitation of existing coherence models is that training on one domain does not easily generalize to unseen categories of text. Previous work advocates for generative models for cross-domain generalization, because for discriminative models, the space of incoherent sentence orderings to discriminate against during training is prohibitively large. In this work, we propose a local discriminative neural model with a much smaller negative sampling space that can efficiently learn against incorrect orderings. The proposed coherence model is simple in structure, yet it significantly outperforms previous state-of-art methods on a standard benchmark dataset on the Wall Street Journal corpus, as well as in multiple new challenging settings of transfer to unseen categories of discourse on Wikipedia articles.
%R 10.18653/v1/P19-1067
%U https://aclanthology.org/P19-1067
%U https://doi.org/10.18653/v1/P19-1067
%P 678-687
Markdown (Informal)
[A Cross-Domain Transferable Neural Coherence Model](https://aclanthology.org/P19-1067) (Xu et al., ACL 2019)
ACL
- Peng Xu, Hamidreza Saghir, Jin Sung Kang, Teng Long, Avishek Joey Bose, Yanshuai Cao, and Jackie Chi Kit Cheung. 2019. A Cross-Domain Transferable Neural Coherence Model. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 678–687, Florence, Italy. Association for Computational Linguistics.