Towards generative aspect-based sentiment analysis

• Main Authors: ZHANG, Wenxuan, LI, Xin, DENG, Yang, BING, Lidong, LAM, Wai
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2021
• Subjects: Analysis problems; Benchmark datasets; Classification networks; Label semantics; Sentiment analysis; State of the art; Task-specific models; Text generations; Training process; Databases and Information Systems; Graphics and Human Computer Interfaces; Information Security
• Online Access: https://ink.library.smu.edu.sg/sis_research/9112; https://ink.library.smu.edu.sg/context/sis_research/article/10115/viewcontent/2021.acl_short.64.pdf
• Institution: Singapore Management University

Aspect-based sentiment analysis (ABSA) has received increasing attention recently. Most existing work tackles ABSA in a discriminative manner, designing various task-specific classification networks for the prediction. Despite their effectiveness, these methods ignore the rich label semantics in ABSA problems and require extensive task-specific designs. In this paper, we propose to tackle various ABSA tasks in a unified generative framework. Two types of paradigms, namely annotation-style and extraction-style modeling, are designed to enable the training process by formulating each ABSA task as a text generation problem. We conduct experiments on four ABSA tasks across multiple benchmark datasets where our proposed generative approach achieves new state-of-the-art results in almost all cases. This also validates the strong generality of the proposed framework which can be easily adapted to arbitrary ABSA task without additional taskspecific model design.1.