While structure learning achieves remarkable performance in high-resource
languages, the situation differs for under-represented languages due to the
scarcity of annotated data. This study focuses on assessing the efficacy of
transfer learning in enhancing dependency parsing for Javanese, a language
spoken by 80 million individuals but characterized by limited representation in
natural language processing. We utilized the Universal Dependencies dataset
consisting of dependency treebanks from more than 100 languages, including
Javanese. We propose two learning strategies to train the model: transfer
learning (TL) and hierarchical transfer learning (HTL). While TL only uses a
source language to pre-train the model, the HTL method uses a source language
and an intermediate language in the learning process. The results show that our
best model uses the HTL method, which improves performance with an increase of
10% for both UAS and LAS evaluations compared to the baseline model.

本研究旨在评估迁移学习在增强爪哇语的依存解析中的功效。使用了包括爪哇语在内的 100 多种语言的依存树库，提出了两种学习策略：迁移学习和分层迁移学习。结果表明，我们的最佳模型采用了分层迁移学习方法，相比基准模型，UAS 和 LAS 评估指标均提高了 10%。