用多任务学习和大模型生成数据，提升低资源语言的词素分割准确率。
Learning Beyond Limits: Multitask Learning and Synthetic Data for Low-Resource Canonical Morpheme Segmentation
- 通过共享语言表示，联合预测词素与释义，增强泛化能力。
- 在多个低资源语言上，词级分割准确率和词素级F1值均显著提升。
- 适合需要少样本词素分析的研究者，尤其关注低资源语言处理。
我们提出一种基于Transformer的词素分割系统，通过多任务学习和大语言模型（LLM）生成的合成数据来增强低资源训练信号。该框架联合从正字法输入中预测形态学片段与释义，利用共同文档处理过程获得的共享语言表征以提升模型泛化能力。为应对数据稀缺问题，我们引入通过上下文学习生成的合成训练数据。在SIGMORPHON 2023数据集上的实验表明，该方法在多个低资源语言上显著提升了词级分割准确率和词素级F1分数。
原文摘要 · Abstract (English)
We introduce a transformer-based morpheme segmentation system that augments a low-resource training signal through multitask learning and LLM-generated synthetic data. Our framework jointly predicts morphological segments and glosses from orthographic input, leveraging shared linguistic representations obtained through a common documentary process to enhance model generalization. To further address data scarcity, we integrate synthetic training data generated by large language models (LLMs) using in-context learning. Experimental results on the SIGMORPHON 2023 dataset show that our approach significantly improves word-level segmentation accuracy and morpheme-level F1-score across multiple low-resource languages.
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