利用约束解码改进生成式跨语言基于方面的情感分析
Improving Generative Cross-lingual Aspect-Based Sentiment Analysis with Constrained Decoding
AI总结：
本文提出基于序列到序列模型的约束解码方法，解决跨语言ABSA问题，无需外部翻译工具，在七种语言和六个任务上超越现有方法，并评估LLMs在不同场景下的表现。
摘要：
尽管基于方面的情感分析（ABSA）取得了显著进展，但低资源语言仍面临挑战，这些语言常因优先考虑英语而被忽视。当前的跨语言ABSA方法局限于有限且较简单的任务，且常依赖外部翻译工具。本文提出了一种新方法，将约束解码与序列到序列模型结合，无需不可靠的翻译工具，并在最复杂任务上将跨语言性能平均提升5%。该方法支持多任务处理，能用单一模型解决多个ABSA任务，约束解码使结果提升超过10%。我们在七种语言和六个ABSA任务上评估该方法，超越现有技术并为此前未探索的任务设定新基准。此外，我们评估了大语言模型（LLMs）在零样本、少样本和微调场景下的表现。LLMs在零样本和少样本设置下表现不佳，微调虽取得与小型多语言模型相当的结果，但训练和推理时间更长。我们为实际应用提供实用建议，加深对跨语言ABSA方法的理解。本研究揭示了跨语言ABSA方法的优势与局限，推动了这一挑战性研究领域的最先进水平。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
