@inproceedings{betti-etal-2020-controlled,
title = "Controlled Text Generation with Adversarial Learning",
author = "Betti, Federico and
Ramponi, Giorgia and
Piccardi, Massimo",
editor = "Davis, Brian and
Graham, Yvette and
Kelleher, John and
Sripada, Yaji",
booktitle = "Proceedings of the 13th International Conference on Natural Language Generation",
month = dec,
year = "2020",
address = "Dublin, Ireland",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.inlg-1.5",
doi = "10.18653/v1/2020.inlg-1.5",
pages = "29--34",
abstract = "In recent years, generative adversarial networks (GANs) have started to attain promising results also in natural language generation. However, the existing models have paid limited attention to the semantic coherence of the generated sentences. For this reason, in this paper we propose a novel network {--} the Controlled TExt generation Relational Memory GAN (CTERM-GAN) {--} that uses an external input to influence the coherence of sentence generation. The network is composed of three main components: a generator based on a Relational Memory conditioned on the external input; a syntactic discriminator which learns to discriminate between real and generated sentences; and a semantic discriminator which assesses the coherence with the external conditioning. Our experiments on six probing datasets have showed that the model has been able to achieve interesting results, retaining or improving the syntactic quality of the generated sentences while significantly improving their semantic coherence with the given input.",
}
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<abstract>In recent years, generative adversarial networks (GANs) have started to attain promising results also in natural language generation. However, the existing models have paid limited attention to the semantic coherence of the generated sentences. For this reason, in this paper we propose a novel network – the Controlled TExt generation Relational Memory GAN (CTERM-GAN) – that uses an external input to influence the coherence of sentence generation. The network is composed of three main components: a generator based on a Relational Memory conditioned on the external input; a syntactic discriminator which learns to discriminate between real and generated sentences; and a semantic discriminator which assesses the coherence with the external conditioning. Our experiments on six probing datasets have showed that the model has been able to achieve interesting results, retaining or improving the syntactic quality of the generated sentences while significantly improving their semantic coherence with the given input.</abstract>
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%0 Conference Proceedings
%T Controlled Text Generation with Adversarial Learning
%A Betti, Federico
%A Ramponi, Giorgia
%A Piccardi, Massimo
%Y Davis, Brian
%Y Graham, Yvette
%Y Kelleher, John
%Y Sripada, Yaji
%S Proceedings of the 13th International Conference on Natural Language Generation
%D 2020
%8 December
%I Association for Computational Linguistics
%C Dublin, Ireland
%F betti-etal-2020-controlled
%X In recent years, generative adversarial networks (GANs) have started to attain promising results also in natural language generation. However, the existing models have paid limited attention to the semantic coherence of the generated sentences. For this reason, in this paper we propose a novel network – the Controlled TExt generation Relational Memory GAN (CTERM-GAN) – that uses an external input to influence the coherence of sentence generation. The network is composed of three main components: a generator based on a Relational Memory conditioned on the external input; a syntactic discriminator which learns to discriminate between real and generated sentences; and a semantic discriminator which assesses the coherence with the external conditioning. Our experiments on six probing datasets have showed that the model has been able to achieve interesting results, retaining or improving the syntactic quality of the generated sentences while significantly improving their semantic coherence with the given input.
%R 10.18653/v1/2020.inlg-1.5
%U https://aclanthology.org/2020.inlg-1.5
%U https://doi.org/10.18653/v1/2020.inlg-1.5
%P 29-34
Markdown (Informal)
[Controlled Text Generation with Adversarial Learning](https://aclanthology.org/2020.inlg-1.5) (Betti et al., INLG 2020)
ACL
- Federico Betti, Giorgia Ramponi, and Massimo Piccardi. 2020. Controlled Text Generation with Adversarial Learning. In Proceedings of the 13th International Conference on Natural Language Generation, pages 29–34, Dublin, Ireland. Association for Computational Linguistics.