T3L: 面向跨语言文本分类的“翻译-测试”迁移学习方法
T3L: Translate-and-Test Transfer Learning for Cross-Lingual Text Classification
AI总结：
针对多语言预训练模型在跨语言文本分类中性能不稳定的问题，提出结合软翻译与端到端微调的翻译-测试迁移学习方法T3L，在三个数据集上实现性能显著提升。
摘要：
跨语言文本分类利用在高资源语言上训练的文本分类器，在无需或仅需极少微调（零样本/少样本跨语言迁移）的情况下，实现其他语言的文本分类任务。当前，跨语言文本分类器通常基于在多种目标语言上预训练的大规模多语言语言模型（LM）构建。然而，这类模型的性能在不同语言和分类任务间差异显著，表明语言建模与分类任务的叠加并非始终有效。为此，本文提出重新审视经典的“翻译-测试”流水线，将翻译与分类阶段清晰分离。该方法结合两部分：1）将目标语言翻译为高资源语言的神经机器翻译器；2）在高资源语言上训练的文本分类器，且神经机器翻译器生成“软”翻译，以支持流水线微调过程中的端到端反向传播。研究在三个跨语言文本分类数据集（XNLI、MLDoc 和 MultiEURLEX）上开展了大量实验，结果表明，所提方法的性能较有竞争力的基线有显著提升。
英文摘要：
Cross-lingual text classification leverages text classifiers trained in a high-resource language to perform text classification in other languages with no or minimal fine-tuning (zero/few-shots cross-lingual transfer). Nowadays, cross-lingual text classifiers are typically built on large-scale, multilingual language models (LMs) pretrained on a variety of languages of interest. However, the performance of these models vary significantly across languages and classification tasks, suggesting that the superposition of the language modelling and classification tasks is not always effective. For this reason, in this paper we propose revisiting the classic "translate-and-test" pipeline to neatly separate the translation and classification stages. The proposed approach couples 1) a neural machine translator translating from the targeted language to a high-resource language, with 2) a text classifier trained in the high-resource language, but the neural machine translator generates "soft" translations to permit end-to-end backpropagation during fine-tuning of the pipeline. Extensive experiments have been carried out over three cross-lingual text classification datasets (XNLI, MLDoc and MultiEURLEX), with the results showing that the proposed approach has significantly improved performance over a competitive baseline.
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。
