Translation Quality Estimation for the IT Domain using Knowledge Distillation Approach (Record no. 11021)

MARC details
000 -LEADER
fixed length control field 02880nam a22002657a 4500
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 201210b2022 a|||f bm|| 00| 0 eng d
024 7# - Author Identifier
Standard number or code https://orcid.org/0000-0003-3874-805X
Source of number or code ORCID
040 ## - CATALOGING SOURCE
Original cataloging agency EG-CaNU
Transcribing agency EG-CaNU
041 0# - Language Code
Language code of text eng
Language code of abstract eng
-- ARA
082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER
Classification number 610
100 0# - MAIN ENTRY--PERSONAL NAME
Personal name Amal Abdelsalam Mahmoud Mohamed
245 1# - TITLE STATEMENT
Title Translation Quality Estimation for the IT Domain using Knowledge Distillation Approach
Statement of responsibility, etc. /Amal Abdelsalam Mahmoud Mohamed
260 ## - PUBLICATION, DISTRIBUTION, ETC.
Date of publication, distribution, etc. 2022
300 ## - PHYSICAL DESCRIPTION
Extent p.
Other physical details ill.
Dimensions 21 cm.
500 ## - GENERAL NOTE
Materials specified Supervisor: Mohamed El-Helw
502 ## - Dissertation Note
Dissertation type Thesis (M.A.)—Nile University, Egypt, 2022 .
504 ## - Bibliography
Bibliography "Includes bibliographical references"
505 0# - Contents
Formatted contents note Contents:
520 3# - Abstract
Abstract Abstract:<br/>Machine Translation (MT) plays a vital role in overcoming language barriers in today’s interconnected world, producing vast streams of translated text used in businesses and daily life. Traditional evaluation methods rely on human-generated reference translations are no longer practical, creating an urgent need for automatic Quality Estimation (QE) systems to assess translation quality. While recent advances in Deep Learning (DL) have significantly improved MT systems, QE systems still face challenges in domain-specific and low-resource settings. Addressing these gaps is crucial to enable reliable and cost-effective QE systems for real-world use. Recognizing that building QE models requires a curated model design, this thesis proposed using knowledge distillation approach to build bilingual distributed representations for training a light QE neural model. The proposed model design aimed to generate bilingual representations able to embed deep semantics and linguistics for the language pair used for translation into a single vector space. Additionally, with the capabilities of knowledge distillation as a model compression technique, the proposed design is aimed to enable the adoption of the QE model in real-world applications. The model is evaluated on the sentence level QE in the Information Technology (IT) domain datasets provided by the Machine Translation Community (WMT). The model performance outperforms strong QE systems that are based on complex deep networks and ensemble models. It achieves the best performance on the WMT IT-domain QE data versions of 2016 and 2017. And it achieves the third best reported correlation on the WMT IT-domain QE data version of 2018. Additionally, the proposed model reduces the QE model size to one-third of that of existing QE ensemble models. With these achievements, this research proved offering scalable and efficient solutions for real-world applications in the field of low-resource QE.<br/>
546 ## - Language Note
Language Note Text in English, abstracts in English and Arabic
650 #4 - Subject
Subject InformaticsIFM
655 #7 - Index Term-Genre/Form
Source of term NULIB
focus term Dissertation, Academic
690 ## - Subject
School InformaticsIFM
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Source of classification or shelving scheme Dewey Decimal Classification
Koha item type Thesis
655 #7 - Index Term-Genre/Form
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Holdings
Withdrawn status Lost status Source of classification or shelving scheme Damaged status Not for loan Home library Current library Date acquired Total Checkouts Full call number Date last seen Price effective from Koha item type
    Dewey Decimal Classification     Main library Main library 07/19/2025   610/A.M.T/2022 07/19/2025 07/19/2025 Thesis