Indo LEGO-ABSA：面向印尼语的多任务生成式方面级情感分析
Indo LEGO-ABSA: A Multitask Generative Aspect Based Sentiment Analysis for Indonesian Language
AI总结：
针对印尼语缺乏生成式多任务方面级情感分析方案的问题，本研究基于mT5构建Indo LEGO-ABSA模型，采用多任务学习与提示方法，在印尼酒店领域数据集的五类ABSA任务上取得了79.55%-88.09%的F1值。
摘要：
方面级情感分析是自然语言处理中的一类方法，旨在识别并理解与实体特定方面相关的情感。方面是代表特定实体的某个维度或属性的词或短语。已有研究利用生成式预训练语言模型开展方面级情感分析任务。LEGO-ABSA是一种已成功将生成式预训练语言模型应用于方面级情感分析的框架，尤其在英语场景下效果显著，该框架采用多任务学习与提示工程方法提升模型性能。但该方法尚未在印尼语（Bahasa Indonesia）语境下落地应用。因此，本研究旨在借助生成式预训练语言模型，将多任务学习与提示方法应用于印尼语方面级情感分析任务。本研究构建了Indo LEGO-ABSA模型，这是一款基于生成式预训练语言模型、通过多任务学习与提示训练的方面级情感分析模型，使用印尼语酒店领域数据集进行训练。实验所得结果如下：方面情感三元组抽取任务F1值为79.55%，统一方面级情感分析任务F1值为86.09%，方面-观点对抽取任务F1值为79.85%，方面词抽取任务F1值为87.45%，观点词抽取任务F1值为88.09%。Indo LEGO-ABSA沿用了采用T5模型（具体为mT5）的LEGO-ABSA框架，通过多任务学习对方面级情感分析下的所有任务进行联合训练。
英文摘要：
Aspect-based sentiment analysis is a method in natural language processing aimed at identifying and understanding sentiments related to specific aspects of an entity. Aspects are words or phrases that represent an aspect or attribute of a particular entity. Previous research has utilized generative pre-trained language models to perform aspect-based sentiment analysis. LEGO-ABSA is one framework that has successfully employed generative pre-trained language models in aspect-based sentiment analysis, particularly in English. LEGO-ABSA uses a multitask learning and prompting approach to enhance model performance. However, the application of this approach has not been done in the context of Bahasa Indonesia. Therefore, this research aims to implement the multitask learning and prompting approach in aspect-based sentiment analysis for Bahasa Indonesia using generative pre-trained language models. In this study, the Indo LEGO-ABSA model is developed, which is an aspect-based sentiment analysis model utilizing generative pre-trained language models and trained with multitask learning and prompting. Indo LEGO-ABSA is trained with a hotel domain dataset in the Indonesian language. The obtained results include an f1-score of 79.55% for the Aspect Sentiment Triplet Extraction task, 86.09% for Unified Aspect-based Sentiment Analysis, 79.85% for Aspect Opinion Pair Extraction, 87.45% for Aspect Term Extraction, and 88.09% for Opinion Term Extraction. Indo LEGO-ABSA adopts the LEGO-ABSA framework that employs the T5 model, specifically mT5, by applying multitask learning to train all tasks within aspect-based sentiment analysis.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
