Learning from a Neighbor: Adapting a Japanese Parser for Korean Through Feature Transfer Learning

We present a new dependency parsing method for Korean applying cross-lingual transfer learning and domain adaptation techniques. Unlike existing transfer learning methods relying on aligned corpora or bilingual lexicons, we propose a feature transfer learning method with minimal supervision, which adapts an existing parser to the target language by transferring the features for the source language to the target language. Specifically, we utilize the Triplet/Quadruplet Model, a hybrid parsing algorithm for Japanese, and apply a delexicalized feature transfer for Korean. Experiments with Penn Korean Treebank show that even using only the transferred features from Japanese achieves a high accuracy (81.6%) for Korean dependency parsing. Further improvements were obtained when a small annotated Korean corpus was combined with the Japanese training corpus, confirming that efficient crosslingual transfer learning can be achieved without expensive linguistic resources.