@inproceedings{cho-etal-2019-towards,
title = "Towards Coherent and Cohesive Long-form Text Generation",
author = "Cho, Woon Sang and
Zhang, Pengchuan and
Zhang, Yizhe and
Li, Xiujun and
Galley, Michel and
Brockett, Chris and
Wang, Mengdi and
Gao, Jianfeng",
editor = "Bamman, David and
Chaturvedi, Snigdha and
Clark, Elizabeth and
Fiterau, Madalina and
Iyyer, Mohit",
booktitle = "Proceedings of the First Workshop on Narrative Understanding",
month = jun,
year = "2019",
address = "Minneapolis, Minnesota",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W19-2401",
doi = "10.18653/v1/W19-2401",
pages = "1--11",
abstract = "Generating coherent and cohesive long-form texts is a challenging task. Previous works relied on large amounts of human-generated texts to train neural language models. However, few attempted to explicitly improve neural language models from the perspectives of coherence and cohesion. In this work, we propose a new neural language model that is equipped with two neural discriminators which provide feedback signals at the levels of sentence (cohesion) and paragraph (coherence). Our model is trained using a simple yet efficient variant of policy gradient, called {`}negative-critical sequence training{'}, which is proposed to eliminate the need of training a separate critic for estimating {`}baseline{'}. Results demonstrate the effectiveness of our approach, showing improvements over the strong baseline {--} recurrent attention-based bidirectional MLE-trained neural language model.",
}
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<abstract>Generating coherent and cohesive long-form texts is a challenging task. Previous works relied on large amounts of human-generated texts to train neural language models. However, few attempted to explicitly improve neural language models from the perspectives of coherence and cohesion. In this work, we propose a new neural language model that is equipped with two neural discriminators which provide feedback signals at the levels of sentence (cohesion) and paragraph (coherence). Our model is trained using a simple yet efficient variant of policy gradient, called ‘negative-critical sequence training’, which is proposed to eliminate the need of training a separate critic for estimating ‘baseline’. Results demonstrate the effectiveness of our approach, showing improvements over the strong baseline – recurrent attention-based bidirectional MLE-trained neural language model.</abstract>
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%0 Conference Proceedings
%T Towards Coherent and Cohesive Long-form Text Generation
%A Cho, Woon Sang
%A Zhang, Pengchuan
%A Zhang, Yizhe
%A Li, Xiujun
%A Galley, Michel
%A Brockett, Chris
%A Wang, Mengdi
%A Gao, Jianfeng
%Y Bamman, David
%Y Chaturvedi, Snigdha
%Y Clark, Elizabeth
%Y Fiterau, Madalina
%Y Iyyer, Mohit
%S Proceedings of the First Workshop on Narrative Understanding
%D 2019
%8 June
%I Association for Computational Linguistics
%C Minneapolis, Minnesota
%F cho-etal-2019-towards
%X Generating coherent and cohesive long-form texts is a challenging task. Previous works relied on large amounts of human-generated texts to train neural language models. However, few attempted to explicitly improve neural language models from the perspectives of coherence and cohesion. In this work, we propose a new neural language model that is equipped with two neural discriminators which provide feedback signals at the levels of sentence (cohesion) and paragraph (coherence). Our model is trained using a simple yet efficient variant of policy gradient, called ‘negative-critical sequence training’, which is proposed to eliminate the need of training a separate critic for estimating ‘baseline’. Results demonstrate the effectiveness of our approach, showing improvements over the strong baseline – recurrent attention-based bidirectional MLE-trained neural language model.
%R 10.18653/v1/W19-2401
%U https://aclanthology.org/W19-2401
%U https://doi.org/10.18653/v1/W19-2401
%P 1-11
Markdown (Informal)
[Towards Coherent and Cohesive Long-form Text Generation](https://aclanthology.org/W19-2401) (Cho et al., WNU 2019)
ACL
- Woon Sang Cho, Pengchuan Zhang, Yizhe Zhang, Xiujun Li, Michel Galley, Chris Brockett, Mengdi Wang, and Jianfeng Gao. 2019. Towards Coherent and Cohesive Long-form Text Generation. In Proceedings of the First Workshop on Narrative Understanding, pages 1–11, Minneapolis, Minnesota. Association for Computational Linguistics.