![](/image/LogoVKU.png)
Please use this identifier to cite or link to this item:
https://elib.vku.udn.vn/handle/123456789/2299
Title: | Improving English-Vietnamese neural machine translation with linguistic factors |
Authors: | Duong, Minh Hung Le, Manh Thanh |
Keywords: | Neural machine translation Encoder-decoder Part-of-speech named entity Word cluster |
Issue Date: | Jul-2022 |
Publisher: | Da Nang Publishing House |
Abstract: | Deep learning has been shown to be successful in a number of domains of natural language processing, especially neural machine translation (NMT). Though promising, NMT still lacks the ability of modeling deeper semantic and syntactic aspects of the language. In this paper, explore different linguistic annotations at the word level, including: part-of-speech, named entity, and word cluster to integrate into the NMT framework. Experiments show that adding these linguistic factors will help the NMT models in reducing language ambiguity or alleviating data sparseness problems. |
Description: | The 11th Conference on Information Technology and its Applications; Topic: Image and Natural Language Processing; pp.128-135. |
URI: | http://elib.vku.udn.vn/handle/123456789/2299 |
ISSN: | 978-604-84-6711-1 |
Appears in Collections: | CITA 2022 |
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.