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fMRI Semantic Category Decoding Using Linguistic Encoding of Word Embeddings Read chapter Read first chapter

2018 | OriginalPaper | Chapter

Hybridized Character-Word Embedding for Korean Traditional Document Translation

Authors : Hosang Yu, Gil-Jin Jang, Minho Lee

Published in: Neural Information Processing

Publisher: Springer International Publishing

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Abstract

Translating traditional documents is quite laborious and time consuming for human translators owing to the voluminous nature and a complexity of grammatical patterns. In recent times, a neural network-based machine translation architecture such as sequence-to-sequence (seq2seq) model showed superior performance in translation. However, it suffers out-of-vocabulary (OOV) issue when dealing with very complex and vocabulary languages such as Chinese characters, resulting in performance degradation. To cope with the OOV issue, we propose a new method by combining word embedding and character embedding to supplement loss from unknown words with character embedding. Experimental results show that the proposed method is efficient to translate old Korean archives (Hanja) to modern Korean documents (Hangul).

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Metadata
Title
Hybridized Character-Word Embedding for Korean Traditional Document Translation
Authors
Hosang Yu
Gil-Jin Jang
Minho Lee
Copyright Year
2018
Book
Neural Information Processing

Print ISBN: 978-3-030-04181-6

Electronic ISBN: 978-3-030-04182-3

Copyright Year: 2018

DOI
https://doi.org/10.1007/978-3-030-04182-3_8

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