提出测试时代码切换方法，提升跨语言情感三元组抽取效果
Test-Time Code-Switching for Cross-lingual Aspect Sentiment Triplet Extraction
- 训练时用双语混合数据生成模型，测试时动态切换语言增强预测
- 在四个语言数据集上平均提升3.7%的加权F1分数
- 小模型经微调后超越ChatGPT和GPT-4，适合低资源语言任务
方面情感三元组抽取（ASTE）在高资源语言上已取得显著成果，但跨语言迁移应用仍较少。现有代码切换方法存在词边界检测与词汇表外问题。本文提出一种新的测试时代码切换（TT-CSW）框架，连接双语训练与单语测试阶段。训练阶段基于双语混写数据构建生成模型，可对双语输入输出双语三元组；测试阶段采用基于对齐的代码切换技术进行测试时增强。在多个跨语言ASTE数据集上的实验验证了方法有效性：在四种不同语言的数据集上平均提升3.7%的加权平均F1值。此外，以ChatGPT和GPT-4为基准，经本框架微调的小型生成模型分别超越其14.2%和5.0%。
原文摘要 · Abstract (English)
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.
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