A Burmese Dependency Parsing Method Based on Transfer Learning

Abstract

Dependency parsing is a fundmental task in natural language processing(NLP). Burmese belongs to a low resource language with a special language structure, therefore, it exists the problems with extremely lacking of high quality data for Burmese dependency parsing and the inaccurate representation of semantic. We propose a Burmese dependency parsing model based on transfer learning, our method generate partially accurate Burmese dependency parsing data by constructing the relationship of English-Burmese. The embedding of Burmese represented by syllables and words to obtain accurate bilingual word vectors representation of English-Burmese. To verify the effectiveness of our method, during the training process, we fuse the dependency parsing data of Burmese and English, which transfer the dependency arc and POS tagging of English to Burmese. The experimental results show that our proposed method has a UAS value of 44.10% and a LAS value of 30.01% on Burmese datadset.