@inproceedings{yuru-etal-2024-ji,
title = "基于逻辑推理和多任务融合的认知刺激对话生成方法(Cognitive stimulation dialogue generation method based on logical reasoning and multi-task integration)",
author = "Jiang, Yuru and
Li, Mengyuan and
Tao, Yuyang and
Qu, Keming and
She, Zepeng and
Shi, Shuicai",
editor = "Maosong, Sun and
Jiye, Liang and
Xianpei, Han and
Zhiyuan, Liu and
Yulan, He",
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-1.8/",
pages = "98--109",
language = "zho",
abstract = "``在全球老龄化背景下,带有认知刺激的对话系统是保持老年人认知健康的重要手段。中文认知刺激对话数据集(Chinese Cognitive Stimulation Conversation Dataset,CSConv)和模型构建的研究工作刚刚开始。本文将认知刺激对话生成视为一个多任务融合的逻辑思维推理过程,将情感分类任务、决策任务和对话回复生成任务间的逻辑关系,建模为一个推理过程,来引导大语言模型生成。针对决策任务,本文提出分层编码器结构的决策模型。决策实验结果表明,决策模型有效的提高了决策任务的准确率。针对多任务过程,本文提出多任务融合方法,将三个任务对应的模型结合在一起。生成实验结果表明,分类、决策及生成的多任务融合方法,显著提升了对话回复能力,证明了该方法的有效性和先进性。''"
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<abstract>“在全球老龄化背景下,带有认知刺激的对话系统是保持老年人认知健康的重要手段。中文认知刺激对话数据集(Chinese Cognitive Stimulation Conversation Dataset,CSConv)和模型构建的研究工作刚刚开始。本文将认知刺激对话生成视为一个多任务融合的逻辑思维推理过程,将情感分类任务、决策任务和对话回复生成任务间的逻辑关系,建模为一个推理过程,来引导大语言模型生成。针对决策任务,本文提出分层编码器结构的决策模型。决策实验结果表明,决策模型有效的提高了决策任务的准确率。针对多任务过程,本文提出多任务融合方法,将三个任务对应的模型结合在一起。生成实验结果表明,分类、决策及生成的多任务融合方法,显著提升了对话回复能力,证明了该方法的有效性和先进性。”</abstract>
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%0 Conference Proceedings
%T 基于逻辑推理和多任务融合的认知刺激对话生成方法(Cognitive stimulation dialogue generation method based on logical reasoning and multi-task integration)
%A Jiang, Yuru
%A Li, Mengyuan
%A Tao, Yuyang
%A Qu, Keming
%A She, Zepeng
%A Shi, Shuicai
%Y Maosong, Sun
%Y Jiye, Liang
%Y Xianpei, Han
%Y Zhiyuan, Liu
%Y Yulan, He
%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 yuru-etal-2024-ji
%X “在全球老龄化背景下,带有认知刺激的对话系统是保持老年人认知健康的重要手段。中文认知刺激对话数据集(Chinese Cognitive Stimulation Conversation Dataset,CSConv)和模型构建的研究工作刚刚开始。本文将认知刺激对话生成视为一个多任务融合的逻辑思维推理过程,将情感分类任务、决策任务和对话回复生成任务间的逻辑关系,建模为一个推理过程,来引导大语言模型生成。针对决策任务,本文提出分层编码器结构的决策模型。决策实验结果表明,决策模型有效的提高了决策任务的准确率。针对多任务过程,本文提出多任务融合方法,将三个任务对应的模型结合在一起。生成实验结果表明,分类、决策及生成的多任务融合方法,显著提升了对话回复能力,证明了该方法的有效性和先进性。”
%U https://aclanthology.org/2024.ccl-1.8/
%P 98-109
Markdown (Informal)
[基于逻辑推理和多任务融合的认知刺激对话生成方法(Cognitive stimulation dialogue generation method based on logical reasoning and multi-task integration)](https://aclanthology.org/2024.ccl-1.8/) (Jiang et al., CCL 2024)
ACL