首页|"六书"多模态处理的形声表征以完善汉语语言模型

"六书"多模态处理的形声表征以完善汉语语言模型

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大型语言模型(LLMs)在自然语言处理中已取得显著成就,但在某些场景下,仍然面临解决中文语言处理复杂性的挑战.本文提出"六书"多模态处理(SWMP)框架,旨在考虑汉语形、声、音、像、意、会特性,便于中文语言多模态处理.在SWMP统一的理论框架下,提出"六书"形声编码(SWPC,简称"六书编码")方法,使得对汉字的表达既能与语法有机结合,又反映汉语灵活应用的特点.文中设计的实验场景包括:(1)实验性建立汉字字根、偏旁(形部)和部件(声部)的图像和"六书"编码(SWPC)的数据库,实现汉语文字和图形的双模态处理;(2)表征若干汉词生成机制,建立提示性问/答模式,进行类比推理.使用SWPC处理中文形态关系数据集(CA8-Mor-10177)的所有问题,精度可达100％.(3)建立"六书"形声编码对词嵌入生成结果微调机制.对中文单词相似度数据集(COS960)中39.37％的问题,相似度计算与人工基础评估结果的平均相对误差低于25％.这些优于目前同类基准精度的结果表明,"六书编码"尝试体现汉语细腻的局部表征和整体关联等特点,可作为对现行汉语语言处理理论和技术的有效补充.
Six-Writings multimodal processing with pictophonetic coding to enhance Chinese language models
While large language models(LLMs)have made significant strides in natural language processing(NLP),they continue to face challenges in adequately addressing the intricacies of the Chinese language in certain scenarios.We propose a framework called Six-Writings multimodal processing(SWMP)to enable direct integration of Chinese NLP(CNLP)with morphological and semantic elements.The first part of SWMP,known as Six-Writings pictophonetic coding(SWPC),is introduced with a suitable level of granularity for radicals and components,enabling effective representation of Chinese characters and words.We conduct several experimental scenarios,including the following:(1)We establish an experimental database consisting of images and SWPC for Chinese characters,enabling dual-mode processing and matrix generation for CNLP.(2)We characterize various generative modes of Chinese words,such as thousands of Chinese idioms,used as question-and-answer(Q&A)prompt functions,facilitating analogies by SWPC.The experiments achieve 100％accuracy in answering all questions in the Chinese morphological data set(CA8-Mor-10177).(3)A fine-tuning mechanism is proposed to refine word embedding results using SWPC,resulting in an average relative error of ≤25％for 39.37％of the questions in the Chinese wOrd Similarity data set(COS960).The results demonstrate that SWMP/SWPC methods effectively capture the distinctive features of Chinese and offer a promising mechanism to enhance CNLP with better efficiency.

Chinese language modelChinese natural language processing(CNLP)Generative language modelMultimodal processingSix-Writings

李伟钢、Mayara C.MARINHO、Denise L.LI、Vitor V.DEOLIVEIRA

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巴西利亚大学计算机科学系(CIC/UnB),巴西巴西利亚市,70910-900

圣保罗大学经济管理会计审计学院(FEA/USP),巴西圣保罗市,05508-010

汉语语言模型 中文自然语言处理 生成式语言模型 多模态处理 六书

Brazilian National Council for Scientific and Technological Development(CNPq)

309545/2021-8

2024

10.1631/FITEE.2300384

信息与电子工程前沿(英文)
浙江大学

信息与电子工程前沿(英文)

CSTPCD
影响因子：0.371
ISSN：2095-9184
年,卷(期)：2024.25(1)
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