Linear‐Time Korean Morphological Analysis Using an Action‐based Local Monotonic Attention Mechanism

• Main Authors: Hyunsun Hwang, Changki Lee
• Format: Article
• Language: English
• Published: Electronics and Telecommunications Research Institute (ETRI) 2019-08-01
• Series: ETRI Journal
• Subjects: deep learning; korean morphological analysis; local attention mechanism; natural language processing; sequence‐to‐sequence learning
• Online Access: https://doi.org/10.4218/etrij.2018-0456

For Korean language processing, morphological analysis is a critical component that requires extensive work. This morphological analysis can be conducted in an end‐to‐end manner without requiring a complicated feature design using a sequence‐to‐sequence model. However, the sequence‐to‐sequence model has a time complexity of O(n2) for an input length n when using the attention mechanism technique for high performance. In this study, we propose a linear‐time Korean morphological analysis model using a local monotonic attention mechanism relying on monotonic alignment, which is a characteristic of Korean morphological analysis. The proposed model indicates an extreme improvement in a single threaded environment and a high morphometric F1‐measure even for a hard attention model with the elimination of the attention mechanism formula.