Neural Machine Translation by Fusing Key Information of Text

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F23%3AUAAQZGWQ" target="_blank" >RIV/00216208:11320/23:UAAQZGWQ - isvavai.cz</a>

Result on the web

<a href="https://www.webofscience.com/wos/woscc/summary/ebad957d-d255-4086-918c-399ee70f7265-bb92b50a/relevance/1" target="_blank" >https://www.webofscience.com/wos/woscc/summary/ebad957d-d255-4086-918c-399ee70f7265-bb92b50a/relevance/1</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.32604/cmc.2023.032732" target="_blank" >10.32604/cmc.2023.032732</a>

Result language

angličtina

Original language name

Neural Machine Translation by Fusing Key Information of Text

Original language description

"When the Transformer proposed by Google in 2017, it was first used for machine translation tasks and achieved the state of the art at that time. Although the current neural machine translation model can generate high quality translation results, there are still mistranslations and omissions in the translation of key information of long sentences. On the other hand, the most important part in traditional translation tasks is the translation of key information. In the translation results, as long as the key information is translated accurately and completely, even if other parts of the results are translated incorrect, the final translation results' quality can still be guaran-teed. In order to solve the problem of mistranslation and missed translation effectively, and improve the accuracy and completeness of long sentence translation in machine translation, this paper proposes a key information fused neural machine translation model based on Transformer. The model proposed in this paper extracts the keywords of the source language text separately as the input of the encoder. After the same encoding as the source language text, it is fused with the output of the source language text encoded by the encoder, then the key information is processed and input into the decoder. With incorporating keyword information from the source language sentence, the model's performance in the task of translating long sentences is very reliable. In order to verify the effectiveness of the method of fusion of key information proposed in this paper, a series of experiments were carried out on the verification set. The experimental results show that the Bilingual Evaluation Understudy (BLEU) score of the model proposed in this paper on the Workshop on Machine Translation (WMT) 2017 test dataset is higher than the BLEU score of Transformer proposed by Google on the WMT2017 test dataset. The experimental results show the advantages of the model proposed in this paper."

Czech name

—

Czech description

—

Type

J_ost - Miscellaneous article in a specialist periodical

CEP classification

—

OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

—

Continuities

—

Publication year

2023

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

"CMC-COMPUTERS MATERIALS & CONTINUA"

ISSN

1546-2218

e-ISSN

—

Volume of the periodical

74

Issue of the periodical within the volume

2

Country of publishing house

US - UNITED STATES

Number of pages

13

Pages from-to

2803-2815

UT code for WoS article

000961024400029

EID of the result in the Scopus database

—