@inproceedings{bao-etal-2022-aeg,
title = "{AEG}: Argumentative Essay Generation via A Dual-Decoder Model with Content Planning",
author = "Bao, Jianzhu and
Wang, Yasheng and
Li, Yitong and
Mi, Fei and
Xu, Ruifeng",
editor = "Goldberg, Yoav and
Kozareva, Zornitsa and
Zhang, Yue",
booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing",
month = dec,
year = "2022",
address = "Abu Dhabi, United Arab Emirates",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.emnlp-main.343",
doi = "10.18653/v1/2022.emnlp-main.343",
pages = "5134--5148",
abstract = "Argument generation is an important but challenging task in computational argumentation. Existing studies have mainly focused on generating individual short arguments, while research on generating long and coherent argumentative essays is still under-explored. In this paper, we propose a new task, Argumentative Essay Generation (AEG).Given a writing prompt, the goal of AEG is to automatically generate an argumentative essay with strong persuasiveness. We construct a large-scale dataset, ArgEssay, for this new task and establish a strong model based on a dual-decoder Transformer architecture. Our proposed model contains two decoders, a planning decoder (PD) and a writing decoder (WD), where PD is used to generate a sequence for essay content planning and WD incorporates the planning information to write an essay. Further, we pre-train this model on a large news dataset to enhance the plan-and-write paradigm. Automatic and human evaluation results show that our model can generate more coherent and persuasive essays with higher diversity and less repetition compared to several baselines.",
}
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<abstract>Argument generation is an important but challenging task in computational argumentation. Existing studies have mainly focused on generating individual short arguments, while research on generating long and coherent argumentative essays is still under-explored. In this paper, we propose a new task, Argumentative Essay Generation (AEG).Given a writing prompt, the goal of AEG is to automatically generate an argumentative essay with strong persuasiveness. We construct a large-scale dataset, ArgEssay, for this new task and establish a strong model based on a dual-decoder Transformer architecture. Our proposed model contains two decoders, a planning decoder (PD) and a writing decoder (WD), where PD is used to generate a sequence for essay content planning and WD incorporates the planning information to write an essay. Further, we pre-train this model on a large news dataset to enhance the plan-and-write paradigm. Automatic and human evaluation results show that our model can generate more coherent and persuasive essays with higher diversity and less repetition compared to several baselines.</abstract>
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%0 Conference Proceedings
%T AEG: Argumentative Essay Generation via A Dual-Decoder Model with Content Planning
%A Bao, Jianzhu
%A Wang, Yasheng
%A Li, Yitong
%A Mi, Fei
%A Xu, Ruifeng
%Y Goldberg, Yoav
%Y Kozareva, Zornitsa
%Y Zhang, Yue
%S Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
%D 2022
%8 December
%I Association for Computational Linguistics
%C Abu Dhabi, United Arab Emirates
%F bao-etal-2022-aeg
%X Argument generation is an important but challenging task in computational argumentation. Existing studies have mainly focused on generating individual short arguments, while research on generating long and coherent argumentative essays is still under-explored. In this paper, we propose a new task, Argumentative Essay Generation (AEG).Given a writing prompt, the goal of AEG is to automatically generate an argumentative essay with strong persuasiveness. We construct a large-scale dataset, ArgEssay, for this new task and establish a strong model based on a dual-decoder Transformer architecture. Our proposed model contains two decoders, a planning decoder (PD) and a writing decoder (WD), where PD is used to generate a sequence for essay content planning and WD incorporates the planning information to write an essay. Further, we pre-train this model on a large news dataset to enhance the plan-and-write paradigm. Automatic and human evaluation results show that our model can generate more coherent and persuasive essays with higher diversity and less repetition compared to several baselines.
%R 10.18653/v1/2022.emnlp-main.343
%U https://aclanthology.org/2022.emnlp-main.343
%U https://doi.org/10.18653/v1/2022.emnlp-main.343
%P 5134-5148
Markdown (Informal)
[AEG: Argumentative Essay Generation via A Dual-Decoder Model with Content Planning](https://aclanthology.org/2022.emnlp-main.343) (Bao et al., EMNLP 2022)
ACL