Abstract
Aspect Sentiment Triplet Extraction (ASTE) is a thriving research area with impressive outcomes being achieved on high-resource languages. However, the application of cross-lingual transfer to the ASTE task has been relatively unexplored, and current code-switching methods still suffer from term boundary detection issues and out-of-dictionary problems. In this study, we introduce a novel Test-Time Code-SWitching (TT-CSW) framework, which bridges the gap between the bilingual training phase and the monolingual test-time prediction. During training, a generative model is developed based on bilingual code-switched training data and can produce bilingual ASTE triplets for bilingual inputs. In the testing stage, we employ an alignment-based code-switching technique for test-time augmentation. Extensive experiments on cross-lingual ASTE datasets validate the effectiveness of our proposed method. We achieve an average improvement of 3.7% in terms of weighted-averaged F1 in four datasets with different languages. Additionally, we set a benchmark using ChatGPT and GPT-4, and demonstrate that even smaller generative models fine-tuned with our proposed TT-CSW framework surpass ChatGPT and GPT-4 by 14.2% and 5.0% respectively.
Abstract (translated)
方面情感三元组抽取（ASTE）是一个充满活力的研究领域，在高资源语言上已经取得了令人瞩目的成果。然而，跨语言迁移在ASTE任务中的应用相对较少探索，当前的代码切换方法仍然存在术语边界检测问题和词典外的问题。在这项研究中，我们引入了一种新颖的测试时间代码切换（TT-CSW）框架，该框架弥合了双语训练阶段与单语测试时预测之间的差距。在训练过程中，基于双语文本代码切换的数据开发了一个生成模型，并且可以为双语输入产生双语ASTE三元组。在测试阶段，我们采用一种基于对齐的代码切换技术进行测试时间增强。跨语言ASTE数据集上的大量实验证明了我们提出方法的有效性。我们在四个不同语言的数据集中实现了加权平均F1分数3.7%的平均改进。此外，我们使用ChatGPT和GPT-4设置了基准，并证明即使是经过我们的TT-CSW框架微调的小型生成模型也分别超越了ChatGPT和GPT-4 14.2% 和5.0%。
URL
https://arxiv.org/abs/2501.14144
