Work

MSMO-ABSA: Multi-Scale and Multi-Objective Optimization for Cross-Lingual Aspect-Based Sentiment Analysis

Year: 2026

Type: conference-paper

Abstract: Aspect-based sentiment analysis (ABSA) garnered growing research interest in multilingual contexts in the past.However, the majority of the studies lack more robust feature alignment and finer aspect-level alignment.In this paper, we propose a novel framework, MSMO: Multi-Scale and Multi-Objective optimization for cross-lingual ABSA.During multi-scale alignment, we achieve cross-lingual sentencelevel and aspect-level alignment, aligning features of aspect terms in different contextual environments.Specifically, we introduce codeswitched bilingual sentences into the language discriminator and consistency training modules to enhance the model's robustness.During multi-objective optimization, we design two optimization objectives: supervised training and consistency training, aiming to enhance cross-lingual semantic alignment.To further improve model performance, we incorporate distilled knowledge of the target language into the model.Results show that MSMO significantly enhances cross-lingual ABSA by achieving state-of-the-art performance across multiple languages and models. 1

Source: Proceedings of the conference - Association for Computational Linguistics. Meeting

DOI: 10.18653/v1/2026.acl-long.851

Authors: Chengyan Wu, Bolei Ma, Ningyuan Deng, He Yanqing, Yun Xue, Liu Xiaoyong

Institutions: South China Normal University, Ludwig-Maximilians-Universität München, Institute of Scientific and Technical Information of China

Language: English

Topic: Sentiment Analysis and Opinion Mining

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

Funders: Guangdong Polytechnic Normal University, National Natural Science Foundation of China, National Science and Technology Major Project, Basic and Applied Basic Research Foundation of Guangdong Province