@inproceedings{secha-etal-2022-ji,
title = "基于预训练及控制码法的藏文律诗自动生成方法(Automatic Generation of {T}ibetan Poems based on Pre-training and Control Code Method)",
author = "Secha, Jia and
Cizhen, Jiacuo and
Cairang, Jia and
Huaguo, Cairang",
editor = "Sun, Maosong and
Liu, Yang and
Che, Wanxiang and
Feng, Yang and
Qiu, Xipeng and
Rao, Gaoqi and
Chen, Yubo",
booktitle = "Proceedings of the 21st Chinese National Conference on Computational Linguistics",
month = oct,
year = "2022",
address = "Nanchang, China",
publisher = "Chinese Information Processing Society of China",
url = "https://aclanthology.org/2022.ccl-1.33",
pages = "366--373",
abstract = "{``}诗歌自动写作研究是自然语言生成的一个重要研究领域,被认为是极具挑战且有趣的任务之一。本文提出一种基于预训练及控制码法的藏文律诗生成方法。在藏文预训练语言模型上进行微调后生成质量显著提升,然而引入控制码法后在很大程度上确保了扣题程度,即关键词在生成诗作中的平均覆盖率居高。此外,在生成诗作中不仅提高词汇的丰富性,而且生成结果的多样性也明显提升。经测试表明,基于预训练及控制码法的生成方法显著优于基线方法。{''}",
language = "Chinese",
}
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<abstract>“诗歌自动写作研究是自然语言生成的一个重要研究领域,被认为是极具挑战且有趣的任务之一。本文提出一种基于预训练及控制码法的藏文律诗生成方法。在藏文预训练语言模型上进行微调后生成质量显著提升,然而引入控制码法后在很大程度上确保了扣题程度,即关键词在生成诗作中的平均覆盖率居高。此外,在生成诗作中不仅提高词汇的丰富性,而且生成结果的多样性也明显提升。经测试表明,基于预训练及控制码法的生成方法显著优于基线方法。”</abstract>
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%0 Conference Proceedings
%T 基于预训练及控制码法的藏文律诗自动生成方法(Automatic Generation of Tibetan Poems based on Pre-training and Control Code Method)
%A Secha, Jia
%A Cizhen, Jiacuo
%A Cairang, Jia
%A Huaguo, Cairang
%Y Sun, Maosong
%Y Liu, Yang
%Y Che, Wanxiang
%Y Feng, Yang
%Y Qiu, Xipeng
%Y Rao, Gaoqi
%Y Chen, Yubo
%S Proceedings of the 21st Chinese National Conference on Computational Linguistics
%D 2022
%8 October
%I Chinese Information Processing Society of China
%C Nanchang, China
%G Chinese
%F secha-etal-2022-ji
%X “诗歌自动写作研究是自然语言生成的一个重要研究领域,被认为是极具挑战且有趣的任务之一。本文提出一种基于预训练及控制码法的藏文律诗生成方法。在藏文预训练语言模型上进行微调后生成质量显著提升,然而引入控制码法后在很大程度上确保了扣题程度,即关键词在生成诗作中的平均覆盖率居高。此外,在生成诗作中不仅提高词汇的丰富性,而且生成结果的多样性也明显提升。经测试表明,基于预训练及控制码法的生成方法显著优于基线方法。”
%U https://aclanthology.org/2022.ccl-1.33
%P 366-373
Markdown (Informal)
[基于预训练及控制码法的藏文律诗自动生成方法(Automatic Generation of Tibetan Poems based on Pre-training and Control Code Method)](https://aclanthology.org/2022.ccl-1.33) (Secha et al., CCL 2022)
ACL