Cross-lingual Transfer Learning for Javanese Dependency Parsing
Fadli Aulawi Al Ghiffari Ika Alfina Kurniawati Azizah
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.