Improve Chinese Aspect Sentiment Quadruplet Prediction via Instruction Learning Based on Large Generate Models  

作　　者：Zhaoliang Wu Yuewei Wu Xiaoli Feng Jiajun Zou Fulian Yin 

机构地区：[1]College of Information and Communication Engineering,Communication University of China,Beijing,100024,China [2]The State Key Laboratory of Media Convergence and Communication,Communication University of China,Beijing,100024,China [3]Department of Electronic Engineering,Tsinghua University,Beijing,100084,China

出　　处：《Computers, Materials & Continua》2024年第3期3391-3412,共22页计算机、材料和连续体（英文）

基　　金：supported by the National Key Research and Development Program(Nos.2021YFF0901705,2021YFF0901700);the State Key Laboratory of Media Convergence and Communication,Communication University of China;the Fundamental Research Funds for the Central Universities;the High-Quality and Cutting-Edge Disciplines Construction Project for Universities in Beijing(Internet Information,Communication University of China).

摘　　要：Aspect-Based Sentiment Analysis(ABSA)is a fundamental area of research in Natural Language Processing(NLP).Within ABSA,Aspect Sentiment Quad Prediction(ASQP)aims to accurately identify sentiment quadruplets in target sentences,including aspect terms,aspect categories,corresponding opinion terms,and sentiment polarity.However,most existing research has focused on English datasets.Consequently,while ASQP has seen significant progress in English,the Chinese ASQP task has remained relatively stagnant.Drawing inspiration from methods applied to English ASQP,we propose Chinese generation templates and employ prompt-based instruction learning to enhance the model’s understanding of the task,ultimately improving ASQP performance in the Chinese context.Ultimately,under the same pre-training model configuration,our approach achieved a 5.79%improvement in the F1 score compared to the previously leading method.Furthermore,when utilizing a larger model with reduced training parameters,the F1 score demonstrated an 8.14%enhancement.Additionally,we suggest a novel evaluation metric based on the characteristics of generative models,better-reflecting model generalization.Experimental results validate the effectiveness of our approach.

关 键 词：ABSA ASQP LLMs sentiment analysis Chinese comments 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]