INVESTIGATING NEURAL MACHINE TRANSLATION QUALITY: AN XCOMET-XL-BASED ANALYSIS ACROSS KATHARINA REISS'S TEXT TYPOLOGIES
Vol.12, Issue 1, 2026, pp. 196-219 Full text: PDF . HTML
DOI: https://doi.org/10.33919/esnbu.26.1.12
Web of Science: [WOS=]
Author:
Betül Özcan Dost https://orcid.org/0000-0003-3110-8017
Affiliation: Ondokuz Mayıs University, Samsun, Türkiye 028k5qw24
Abstract
This study examines AI-driven neural methods that reduce human involvement in machine translation evaluation. Traditional evaluation approaches face challenges related to consistency, scalability, and evaluator subjectivity. To address these limitations, the study employs xCOMET-XL, which evaluates translation quality within a shared semantic space. Within the scope of this study, fifteen Turkish source texts, classified according to Katharina Reiss's typology as informative, expressive, and operative, were translated into English using Google Translate and DeepL. The translations were evaluated using xCOMET-XL, followed by human evaluation. The originality of the study lies in its comparative design examining the alignment between xCOMET-XL scores and human judgments. After comparing results, alignment between the methods was analysed. Findings signal a relatively high degree of alignment with human judgments in certain texts, highlighting neural metrics' potential as a fast, scalable, and systematic alternative. The study contributes to Translation Studies by supporting alternative translation evaluation approaches.
Keywords: machine translation, xCOMET-XL, translation assessment, text types, Katharina Reiss
Article history:
Submitted: 02 May 2026
Reviewed: 09 May 2026
Accepted: 15 May 2026
Published: 20 June 2026
Citation (APA):
Özcan Dost, B. (2026). Investigating neural machine translation quality: An xCOMET-XL-based analysis across Katharina Reiss's text typologies. English Studies at NBU, 12(1), 196-219. https://doi.org/10.33919/esnbu.26.1.12
Copyright © 2026 Betül Özcan Dost
This is an Open Access article published and distributed under the terms of the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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