Improving Generative Cross-lingual Aspect-Based Sentiment Analysis with Constrained Decoding

TL;DR

This paper presents a novel constrained decoding approach for cross-lingual aspect-based sentiment analysis that outperforms existing methods, eliminates reliance on translation tools, and supports multi-tasking across multiple languages and tasks.

Contribution

It introduces a sequence-to-sequence model with constrained decoding for cross-lingual ABSA, improving performance without external translation tools and enabling multi-task learning.

Findings

Achieves 5% average performance improvement on complex tasks

Supports multi-tasking, boosting results by over 10%

Surpasses state-of-the-art methods across seven languages and six tasks

Abstract

While aspect-based sentiment analysis (ABSA) has made substantial progress, challenges remain for low-resource languages, which are often overlooked in favour of English. Current cross-lingual ABSA approaches focus on limited, less complex tasks and often rely on external translation tools. This paper introduces a novel approach using constrained decoding with sequence-to-sequence models, eliminating the need for unreliable translation tools and improving cross-lingual performance by 5\% on average for the most complex task. The proposed method also supports multi-tasking, which enables solving multiple ABSA tasks with a single model, with constrained decoding boosting results by more than 10\%. We evaluate our approach across seven languages and six ABSA tasks, surpassing state-of-the-art methods and setting new benchmarks for previously unexplored tasks. Additionally, we assess large language models (LLMs) in zero-shot, few-shot, and fine-tuning scenarios. While LLMs perform poorly in zero-shot and few-shot settings, fine-tuning achieves competitive results compared to smaller multilingual models, albeit at the cost of longer training and inference times. We provide practical recommendations for real-world applications, enhancing the understanding of cross-lingual ABSA methodologies. This study offers valuable insights into the strengths and limitations of cross-lingual ABSA approaches, advancing the state-of-the-art in this challenging research domain.