Digital Library[ Search Result ]
English-Korean Neural Machine Translation using MASS with Relative Position Representation
Youngjun Jung, Cheoneum Park, Changki Lee, Junseok Kim
http://doi.org/10.5626/JOK.2020.47.11.1038
Neural Machine Translation has been mainly studied for a Sequence-to-Sequence model using supervised learning. However, since the supervised learning method shows low performance when the data is insufficient, recently, a transfer learning method of fine-tuning using the pre-training model based on a large amount of monolingual data such as BERT and MASS has been mainly studied in the field of natural language processing. In this paper, MASS using the pre-training method for language generation, was applied to the English-Korean machine translation. As a result of the experiment, the performance of the English-Korean machine translation model using MASS showed better performance than the existing models, and the performance of the machine translation model was further improved by applying the relative position representation method to MASS.
Korean Text Summarization using MASS with Relative Position Representation
Youngjun Jung, Hyunsun Hwang, Changki Lee
http://doi.org/10.5626/JOK.2020.47.9.873
In the language generation task, deep learning-based models that generate natural languages using a Sequence-to-Sequence model are actively being studied. In the field of text summarization, wherein the method of extracting only the core sentences from the text is used, an abstract summarization study is underway. Recently, a transfer learning method of fine-tuning using pre-training model based on large amount of monolingual data such as BERT and MASS has been mainly studied in the field of natural language processing. In this paper, after pre-training for the Korean language generation using MASS, it was applied to the summarization of the Korean text. As a result of the experiment, the Korean text summarization model using MASS was higher performance than the existing models. Additionally, the performance of the text summarization model was improved by applying the relative position representation method to MASS.
Search
Journal of KIISE
- ISSN : 2383-630X(Print)
- ISSN : 2383-6296(Electronic)
- KCI Accredited Journal
Editorial Office
- Tel. +82-2-588-9240
- Fax. +82-2-521-1352
- E-mail. chwoo@kiise.or.kr