Can I read Context-Aware Neural Machine Translation for Korean Honorific Expressions on EtoBox?

Context-Aware Neural Machine Translation for Korean Honorific Expressions by Yongkeun Hwang; Yanghoon Kim; Kyomin Jung is a Engineering article available to read on EtoBox.

What is Context-Aware Neural Machine Translation for Korean Honorific Expressions about?

Neural machine translation (NMT) is one of the text generation tasks which has achieved significant improvement with the rise of deep neural networks. However, language-specific problems such as handling the translation of honorifics received little attention. In this paper, we propose a context-aware NMT to promote translation improvements of Korean honorifics. By exploiting the information such as the relationship between speakers from the surrounding sentences, our proposed model effectively manages the use of honorific expressions. Specifically, we utilize a novel encoder architecture that can represent the contextual information of the given input sentences. Furthermore, a context-aware post-editing (CAPE) technique is adopted to refine a set of inconsistent sentence-level honorific translations. To demonstrate the efficacy of the proposed method, honorific-labeled test data is required. Thus, we also design a heuristic that labels Korean sentences to distinguish between honorific and non-honorific styles. Experimental results show that our proposed method outperforms sentence-level NMT baselines both in overall translation quality and honorific translations.

Who reads Context-Aware Neural Machine Translation for Korean Honorific Expressions?

Author

Yongkeun Hwang; Yanghoon Kim; Kyomin Jung

Publisher

MDPI AG

Published

2021

Language

EN

Field

Engineering (Physical Sciences)