TY  - JOUR
T1  - Performance Analysis of Korean Morphological Analyzer based on Transformer and BERT
AU  - Choi, Yongseok 
AU  - Lee, Kong Joo 
JO  - Journal of KIISE, JOK
PY  - 2020
DA  - 2020/1/14
DO  - 10.5626/JOK.2020.47.8.730
KW  - sequence-to-sequence
KW  - Korean morphological analyzer
KW  - Transformer
KW  - attention mechanism
KW  - copying mechanism
AB  - This paper introduces a Korean morphological analyzer using the Transformer, which is one of the most popular sequence-to-sequence deep neural models. The Transformer comprises an encoder and a decoder. The encoder compresses a raw input sentence into a fixed-size vector, while the decoder generates a morphological analysis result for the vector. We also replace the encoder with BERT, a pre-trained language representation model. An attention mechanism and a copying mechanism are integrated in the decoder. The processing units of the encoder and the decoder are eojeol-based WordPiece and morpheme-based WordPiece, respectively. Experimental results showed that the Transformer with fine-tuned BERT outperforms the randomly initialized Transformer by 2.9% in the F1 score. We also investigated the effects of the WordPiece embedding on morphological analysis when they are not fully updated in the training phases.