Enhancing the quality of Machine Translation System Using Cross-Lingual Word Embedding Models = Nâng cao chất lượng của hệ thống dịch máy dựa trên các mô hình vector nhúng biểu diễn từ giữa hai ngôn ngữ. Luận văn ThS. Máy tính: 84801
In recent years, Machine Translation has shown promising results and received much interest of researchers. Two approaches that have been widely used for machine translation are Phrase-based Statistical Machine Translation (PBSMT) and Neural Machine Translation (NMT). During translation, both appro...
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| 格式: | Theses and Dissertations |
| 語言: | English |
| 出版: |
2019
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| 主題: | |
| 在線閱讀: | http://repository.vnu.edu.vn/handle/VNU_123/65766 |
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| 機構: | Vietnam National University, Hanoi |
| 語言: | English |
| 總結: | In recent years, Machine Translation has shown promising results and received much
interest of researchers. Two approaches that have been widely used for machine translation are Phrase-based Statistical Machine Translation (PBSMT) and Neural Machine Translation (NMT). During translation, both approaches rely heavily on large
amounts of bilingual corpora which require much effort and financial support. The
lack of bilingual data leads to a poor phrase-table, which is one of the main components of PBSMT, and the unknown word problem in NMT. In contrast, monolingual
data are available for most of the languages. Thanks to the advantage, many models
of word embedding and cross-lingual word embedding have been appeared to improve
the quality of various tasks in natural language processing. The purpose of this thesis
is to propose two models for using cross-lingual word embedding models to address
the above impediment. The first model enhances the quality of the phrase-table in
SMT, and the remaining model tackles the unknown word problem in NMT.
Publications:
? Minh-Thuan Nguyen, Van-Tan Bui, Huy-Hien Vu, Phuong-Thai Nguyen and Chi-Mai Luong.
Enhancing the quality of Phrase-table in Statistical Machine Translation for Less-Common and
Low-Resource Languages. In the 2018 International Conference on Asian Language Processing
(IALP 2018). |
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