无需翻译工具，用约束解码提升多语言情感分析效果。
Advancing Cross-lingual Aspect-Based Sentiment Analysis with LLMs and Constrained Decoding for Sequence-to-Sequence Models
- 用约束解码构建端到端的跨语言情感分析模型。
- 在复杂任务上性能提升最高达10%。
- 适合需要低资源语言支持的工业级应用。
方面级情感分析（ABSA）已取得显著进展，但低资源语言仍面临挑战，因研究多集中于英语。现有跨语言ABSA研究常局限于简单任务，且严重依赖外部翻译工具。本文提出一种新型序列到序列方法，用于复合型ABSA任务，无需翻译工具。该方法通过约束解码，使跨语言ABSA性能提升最高达10%。此方法拓展了跨语言ABSA的应用范围，可处理更复杂任务，并为依赖翻译的技术提供了高效实用的替代方案。此外，我们对比了大语言模型（LLMs），发现微调后的多语言LLM表现相当，但以英语为中心的LLM在此类任务中表现不佳。
原文摘要 · Abstract (English)
Aspect-based sentiment analysis (ABSA) has made significant strides, yet challenges remain for low-resource languages due to the predominant focus on English. Current cross-lingual ABSA studies often centre on simpler tasks and rely heavily on external translation tools. In this paper, we present a novel sequence-to-sequence method for compound ABSA tasks that eliminates the need for such tools. Our approach, which uses constrained decoding, improves cross-lingual ABSA performance by up to 10\%. This method broadens the scope of cross-lingual ABSA, enabling it to handle more complex tasks and providing a practical, efficient alternative to translation-dependent techniques. Furthermore, we compare our approach with large language models (LLMs) and show that while fine-tuned multilingual LLMs can achieve comparable results, English-centric LLMs struggle with these tasks.
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