计算机科学
构造(python库)
水准点(测量)
笔记本电脑
任务(项目管理)
情绪分析
情报检索
块(置换群论)
编码(集合论)
语言模型
索引(排版)
人工智能
自然语言处理
机器学习
万维网
地理
集合(抽象数据类型)
程序设计语言
管理
几何学
经济
操作系统
数学
大地测量学
作者
Tok Wang Ling,Lei Chen,Chen Liao,Shilei Huang,Zhipeng Yu,Yi Liu
标识
DOI:10.1109/apsipaasc58517.2023.10317451
摘要
Aspect sentiment classification (ASC) is an essential subtask of aspect-based sentiment analysis. Recently, pre-trained language models (PLMs) have gradually become the mainstream building block for the ASC task and retrieval-based methods are shown to be effective in helping PLMs better understand various downstream tasks. However, retrieval-based methods need to introduce external knowledge, and building an index of large-scale corpus leads to huge cost. To this end, we propose an effective method that utilizes training data to construct a retrieval corpus and retrieve instances most similar to current input to enhance semantic representations. In the proposed method, three kinds of retrieval metrics are applied, allowing us to quickly adapt to ASC tasks. Experimental results show that this simple retrieval augmented method can achieve significantly better performance on three benchmark ASC datasets (Twitter, Laptop, and Restaurant). Our code is available at https://github.com/rickltt/re-bert.
科研通智能强力驱动
Strongly Powered by AbleSci AI