@inproceedings{yang-etal-2024-ji,
title = "基于问题扩展的散文答案候选句抽取方法研究(Sentiment classification method based on multitasking and multimodal interactive learning)",
author = "Yang, Lei and
Suge, Wang and
Shuqi, Li and
Hao, Wang",
editor = "Sun, Maosong and
Liang, Jiye and
Han, Xianpei and
Liu, Zhiyuan and
He, Yulan",
booktitle = "Proceedings of the 23rd Chinese National Conference on Computational Linguistics (Volume 1: Main Conference)",
month = jul,
year = "2024",
address = "Taiyuan, China",
publisher = "Chinese Information Processing Society of China",
url = "https://aclanthology.org/2024.ccl-1.48/",
pages = "613--624",
language = "zho",
abstract = "{\textquotedblleft}在散文阅读理解中,一方面问题的题干通常较为简洁、用词较为抽象,机器难以直接理解问题的含义和要求;另一方面,散文文章较长,答案候选句分散在文章的多个段落,给答案候选句的抽取任务带来巨大的挑战。因此,本文提出了一种基于问题扩展的散文答案候选句抽取方法。首先,利用大语言模型抽取文章中与问题题干相关的词,构建问题词扩展库,其次,利用大语言模型强大的生成能力对原问题的题干进行重写,进一步,利用问题词扩展库对其扩展,最后,通过对散文文章分块处理,建立基于全局上下文信息、历史信息的问题和文章句子的相关性判断模型,用于抽取答案候选句。通过在散文阅读理解数据集上进行实验,实验结果表明本文提出的方法提高了散文抽取答案候选句的准确率,为散文阅读理解的生成类问题的解答提供了技术支撑。{\textquotedblright}"
}
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<abstract>“在散文阅读理解中,一方面问题的题干通常较为简洁、用词较为抽象,机器难以直接理解问题的含义和要求;另一方面,散文文章较长,答案候选句分散在文章的多个段落,给答案候选句的抽取任务带来巨大的挑战。因此,本文提出了一种基于问题扩展的散文答案候选句抽取方法。首先,利用大语言模型抽取文章中与问题题干相关的词,构建问题词扩展库,其次,利用大语言模型强大的生成能力对原问题的题干进行重写,进一步,利用问题词扩展库对其扩展,最后,通过对散文文章分块处理,建立基于全局上下文信息、历史信息的问题和文章句子的相关性判断模型,用于抽取答案候选句。通过在散文阅读理解数据集上进行实验,实验结果表明本文提出的方法提高了散文抽取答案候选句的准确率,为散文阅读理解的生成类问题的解答提供了技术支撑。”</abstract>
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%0 Conference Proceedings
%T 基于问题扩展的散文答案候选句抽取方法研究(Sentiment classification method based on multitasking and multimodal interactive learning)
%A Yang, Lei
%A Suge, Wang
%A Shuqi, Li
%A Hao, Wang
%Y Sun, Maosong
%Y Liang, Jiye
%Y Han, Xianpei
%Y Liu, Zhiyuan
%Y He, Yulan
%S Proceedings of the 23rd Chinese National Conference on Computational Linguistics (Volume 1: Main Conference)
%D 2024
%8 July
%I Chinese Information Processing Society of China
%C Taiyuan, China
%G zho
%F yang-etal-2024-ji
%X “在散文阅读理解中,一方面问题的题干通常较为简洁、用词较为抽象,机器难以直接理解问题的含义和要求;另一方面,散文文章较长,答案候选句分散在文章的多个段落,给答案候选句的抽取任务带来巨大的挑战。因此,本文提出了一种基于问题扩展的散文答案候选句抽取方法。首先,利用大语言模型抽取文章中与问题题干相关的词,构建问题词扩展库,其次,利用大语言模型强大的生成能力对原问题的题干进行重写,进一步,利用问题词扩展库对其扩展,最后,通过对散文文章分块处理,建立基于全局上下文信息、历史信息的问题和文章句子的相关性判断模型,用于抽取答案候选句。通过在散文阅读理解数据集上进行实验,实验结果表明本文提出的方法提高了散文抽取答案候选句的准确率,为散文阅读理解的生成类问题的解答提供了技术支撑。”
%U https://aclanthology.org/2024.ccl-1.48/
%P 613-624
Markdown (Informal)
[基于问题扩展的散文答案候选句抽取方法研究(Sentiment classification method based on multitasking and multimodal interactive learning)](https://aclanthology.org/2024.ccl-1.48/) (Yang et al., CCL 2024)
ACL