@inproceedings{ouzerrout-2025-uter,
title = "{UTER}: Capturing the Human Touch in Evaluating Morphologically Rich and Low-Resource Languages",
author = "Ouzerrout, Samy",
editor = "Ojha, Atul Kr. and
Liu, Chao-hong and
Vylomova, Ekaterina and
Pirinen, Flammie and
Washington, Jonathan and
Oco, Nathaniel and
Zhao, Xiaobing",
booktitle = "Proceedings of the Eighth Workshop on Technologies for Machine Translation of Low-Resource Languages (LoResMT 2025)",
month = may,
year = "2025",
address = "Albuquerque, New Mexico, U.S.A.",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.loresmt-1.3/",
doi = "10.18653/v1/2025.loresmt-1.3",
pages = "16--23",
ISBN = "979-8-89176-230-5",
abstract = "We introduce UTER, a novel automatic translation evaluation metric specifically designed for morphologically complex languages. Unlike traditional TER approaches, UTER incorporates a reordering algorithm and leverages the S{\o}rensen-Dicse similarity measure to better account for morphological variations.Tested on morphologically rich and low resource languages from the WMT22 dataset, such as Finnish, Estonian, Kazakh, and Xhosa, UTER delivers results that align more closely with human direct assessments (DA) and outperforms benchmark metrics, including chrF and METEOR. Furthermore, its effectiveness has also been demonstrated on languages with complex writing systems, such as Chinese and Japanese, showcasing its versatility and robustness."
}<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="ouzerrout-2025-uter">
<titleInfo>
<title>UTER: Capturing the Human Touch in Evaluating Morphologically Rich and Low-Resource Languages</title>
</titleInfo>
<name type="personal">
<namePart type="given">Samy</namePart>
<namePart type="family">Ouzerrout</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2025-05</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Proceedings of the Eighth Workshop on Technologies for Machine Translation of Low-Resource Languages (LoResMT 2025)</title>
</titleInfo>
<name type="personal">
<namePart type="given">Atul</namePart>
<namePart type="given">Kr.</namePart>
<namePart type="family">Ojha</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Chao-hong</namePart>
<namePart type="family">Liu</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Ekaterina</namePart>
<namePart type="family">Vylomova</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Flammie</namePart>
<namePart type="family">Pirinen</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Jonathan</namePart>
<namePart type="family">Washington</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Nathaniel</namePart>
<namePart type="family">Oco</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Xiaobing</namePart>
<namePart type="family">Zhao</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>Association for Computational Linguistics</publisher>
<place>
<placeTerm type="text">Albuquerque, New Mexico, U.S.A.</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
<identifier type="isbn">979-8-89176-230-5</identifier>
</relatedItem>
<abstract>We introduce UTER, a novel automatic translation evaluation metric specifically designed for morphologically complex languages. Unlike traditional TER approaches, UTER incorporates a reordering algorithm and leverages the Sørensen-Dicse similarity measure to better account for morphological variations.Tested on morphologically rich and low resource languages from the WMT22 dataset, such as Finnish, Estonian, Kazakh, and Xhosa, UTER delivers results that align more closely with human direct assessments (DA) and outperforms benchmark metrics, including chrF and METEOR. Furthermore, its effectiveness has also been demonstrated on languages with complex writing systems, such as Chinese and Japanese, showcasing its versatility and robustness.</abstract>
<identifier type="citekey">ouzerrout-2025-uter</identifier>
<identifier type="doi">10.18653/v1/2025.loresmt-1.3</identifier>
<location>
<url>https://aclanthology.org/2025.loresmt-1.3/</url>
</location>
<part>
<date>2025-05</date>
<extent unit="page">
<start>16</start>
<end>23</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T UTER: Capturing the Human Touch in Evaluating Morphologically Rich and Low-Resource Languages
%A Ouzerrout, Samy
%Y Ojha, Atul Kr.
%Y Liu, Chao-hong
%Y Vylomova, Ekaterina
%Y Pirinen, Flammie
%Y Washington, Jonathan
%Y Oco, Nathaniel
%Y Zhao, Xiaobing
%S Proceedings of the Eighth Workshop on Technologies for Machine Translation of Low-Resource Languages (LoResMT 2025)
%D 2025
%8 May
%I Association for Computational Linguistics
%C Albuquerque, New Mexico, U.S.A.
%@ 979-8-89176-230-5
%F ouzerrout-2025-uter
%X We introduce UTER, a novel automatic translation evaluation metric specifically designed for morphologically complex languages. Unlike traditional TER approaches, UTER incorporates a reordering algorithm and leverages the Sørensen-Dicse similarity measure to better account for morphological variations.Tested on morphologically rich and low resource languages from the WMT22 dataset, such as Finnish, Estonian, Kazakh, and Xhosa, UTER delivers results that align more closely with human direct assessments (DA) and outperforms benchmark metrics, including chrF and METEOR. Furthermore, its effectiveness has also been demonstrated on languages with complex writing systems, such as Chinese and Japanese, showcasing its versatility and robustness.
%R 10.18653/v1/2025.loresmt-1.3
%U https://aclanthology.org/2025.loresmt-1.3/
%U https://doi.org/10.18653/v1/2025.loresmt-1.3
%P 16-23
Markdown (Informal)
[UTER: Capturing the Human Touch in Evaluating Morphologically Rich and Low-Resource Languages](https://aclanthology.org/2025.loresmt-1.3/) (Ouzerrout, LoResMT 2025)
ACL