LoRA Fine-Tuning of English–Norwegian NMT for the Oil & Gas Industry
Xiaojing Yang, Zhihan Li, Gege Sun, Mengyue Li, Meriem Beloucif
Correct Metadata for
Abstract
Adapting large language models to specialized domains remains challenging due to the computational cost of full finetuning and the limited availability of domain-specific parallel data. We present a systematic framework for parameter-efficient domain adaptation using Low-Rank Adaptation (LoRA) geared towards efficient learning in low-resource scenarios. Our method combines data-scaling analysis, dual-track hyperparameter optimization, and competitive benchmarking. We evaluate our approach on the low-resource English–Norwegian petroleum translation domain using a distilled version of NLLB and parallel data from the Norwegian Petroleum Directorate. Our adapted model achieves 61.48 BLEU (+24.62 over the base model) and 0.9298 COMET, while updating <0.4% of parameters. Our results provide a reproducible and computationally efficient blueprint for domain adaptation in neural machine translation, particularly for specialized and resource-constrained domains.- Anthology ID:
- 2026.eamt-1.25
- Volume:
- Proceedings of the 26th Annual Conference of the European Association for Machine Translation (Volume 1)
- Month:
- June
- Year:
- 2026
- Address:
- Tilburg, The Netherlands
- Editors:
- Dimitar Shterionov, Eva Vanmassenhove, Mirella De Sisto, Fred Blain, Javad Pourmostafa Roshan Sharami, Lisa Lepp, Chiara Manna, Argentina Anna Rescigno, Alina Karakanta, Ayla Rigouts Terryn, Manuel Lardelli, Natalia Resende, Elena Murgolo, Janiça Hackenbuchner, Anna Zaretskaya, Miquel Esplà-Gomis, Thierry Etchegoyhen, Dagmar Gromann, Rachel Bawden, Barry Haddow, Sara Szoc, Mikel Forcada, Helena Moniz
- Venue:
- EAMT
- SIG:
- Publisher:
- European Association for Machine Translation
- Note:
- Pages:
- 385–398
- Language:
- URL:
- https://aclanthology.org/2026.eamt-1.25/
- DOI:
- Bibkey:
- Cite (ACL):
- Xiaojing Yang, Zhihan Li, Gege Sun, Mengyue Li, and Meriem Beloucif. 2026. LoRA Fine-Tuning of English–Norwegian NMT for the Oil & Gas Industry. In Proceedings of the 26th Annual Conference of the European Association for Machine Translation (Volume 1), pages 385–398, Tilburg, The Netherlands. European Association for Machine Translation.
- Cite (Informal):
- LoRA Fine-Tuning of English–Norwegian NMT for the Oil & Gas Industry (Yang et al., EAMT 2026)
- Copy Citation:
- PDF:
- https://aclanthology.org/2026.eamt-1.25.pdf
Export citation
@inproceedings{yang-etal-2026-lora,
title = "{L}o{RA} Fine-Tuning of {E}nglish{--}{N}orwegian {NMT} for the Oil {\&} Gas Industry",
author = "Yang, Xiaojing and
Li, Zhihan and
Sun, Gege and
Li, Mengyue and
Beloucif, Meriem",
editor = "Shterionov, Dimitar and
Vanmassenhove, Eva and
De Sisto, Mirella and
Blain, Fred and
Pourmostafa Roshan Sharami, Javad and
Lepp, Lisa and
Manna, Chiara and
Rescigno, Argentina Anna and
Karakanta, Alina and
Rigouts Terryn, Ayla and
Lardelli, Manuel and
Resende, Natalia and
Murgolo, Elena and
Hackenbuchner, Jani{\c{c}}a and
Zaretskaya, Anna and
Espl{\`a}-Gomis, Miquel and
Etchegoyhen, Thierry and
Gromann, Dagmar and
Bawden, Rachel and
Haddow, Barry and
Szoc, Sara and
Forcada, Mikel and
Moniz, Helena",
booktitle = "Proceedings of the 26th Annual Conference of the {E}uropean Association for Machine Translation (Volume 1)",
month = jun,
year = "2026",
address = "Tilburg, The Netherlands",
publisher = "European Association for Machine Translation",
url = "https://aclanthology.org/2026.eamt-1.25/",
pages = "385--398",
ISBN = "9789403901411",
abstract = "Adapting large language models to specialized domains remains challenging due to the computational cost of full finetuning and the limited availability of domain-specific parallel data. We present a systematic framework for parameter-efficient domain adaptation using Low-Rank Adaptation (LoRA) geared towards efficient learning in low-resource scenarios. Our method combines data-scaling analysis, dual-track hyperparameter optimization, and competitive benchmarking. We evaluate our approach on the low-resource English{--}Norwegian petroleum translation domain using a distilled version of NLLB and parallel data from the Norwegian Petroleum Directorate. Our adapted model achieves 61.48 BLEU (+24.62 over the base model) and 0.9298 COMET, while updating {\ensuremath{<}}0.4{\%} of parameters. Our results provide a reproducible and computationally efficient blueprint for domain adaptation in neural machine translation, particularly for specialized and resource-constrained domains."
}<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="yang-etal-2026-lora">
<titleInfo>
<title>LoRA Fine-Tuning of English–Norwegian NMT for the Oil & Gas Industry</title>
</titleInfo>
<name type="personal">
<namePart type="given">Xiaojing</namePart>
<namePart type="family">Yang</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Zhihan</namePart>
<namePart type="family">Li</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Gege</namePart>
<namePart type="family">Sun</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Mengyue</namePart>
<namePart type="family">Li</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Meriem</namePart>
<namePart type="family">Beloucif</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2026-06</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Proceedings of the 26th Annual Conference of the European Association for Machine Translation (Volume 1)</title>
</titleInfo>
<name type="personal">
<namePart type="given">Dimitar</namePart>
<namePart type="family">Shterionov</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Eva</namePart>
<namePart type="family">Vanmassenhove</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Mirella</namePart>
<namePart type="family">De Sisto</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Fred</namePart>
<namePart type="family">Blain</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Javad</namePart>
<namePart type="family">Pourmostafa Roshan Sharami</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Lisa</namePart>
<namePart type="family">Lepp</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Chiara</namePart>
<namePart type="family">Manna</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Argentina</namePart>
<namePart type="given">Anna</namePart>
<namePart type="family">Rescigno</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Alina</namePart>
<namePart type="family">Karakanta</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Ayla</namePart>
<namePart type="family">Rigouts Terryn</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Manuel</namePart>
<namePart type="family">Lardelli</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Natalia</namePart>
<namePart type="family">Resende</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Elena</namePart>
<namePart type="family">Murgolo</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Janiça</namePart>
<namePart type="family">Hackenbuchner</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Anna</namePart>
<namePart type="family">Zaretskaya</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Miquel</namePart>
<namePart type="family">Esplà-Gomis</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Thierry</namePart>
<namePart type="family">Etchegoyhen</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Dagmar</namePart>
<namePart type="family">Gromann</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Rachel</namePart>
<namePart type="family">Bawden</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Barry</namePart>
<namePart type="family">Haddow</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Sara</namePart>
<namePart type="family">Szoc</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Mikel</namePart>
<namePart type="family">Forcada</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Helena</namePart>
<namePart type="family">Moniz</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>European Association for Machine Translation</publisher>
<place>
<placeTerm type="text">Tilburg, The Netherlands</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
<identifier type="isbn">9789403901411</identifier>
</relatedItem>
<abstract>Adapting large language models to specialized domains remains challenging due to the computational cost of full finetuning and the limited availability of domain-specific parallel data. We present a systematic framework for parameter-efficient domain adaptation using Low-Rank Adaptation (LoRA) geared towards efficient learning in low-resource scenarios. Our method combines data-scaling analysis, dual-track hyperparameter optimization, and competitive benchmarking. We evaluate our approach on the low-resource English–Norwegian petroleum translation domain using a distilled version of NLLB and parallel data from the Norwegian Petroleum Directorate. Our adapted model achieves 61.48 BLEU (+24.62 over the base model) and 0.9298 COMET, while updating \ensuremath<0.4% of parameters. Our results provide a reproducible and computationally efficient blueprint for domain adaptation in neural machine translation, particularly for specialized and resource-constrained domains.</abstract>
<identifier type="citekey">yang-etal-2026-lora</identifier>
<location>
<url>https://aclanthology.org/2026.eamt-1.25/</url>
</location>
<part>
<date>2026-06</date>
<extent unit="page">
<start>385</start>
<end>398</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings %T LoRA Fine-Tuning of English–Norwegian NMT for the Oil & Gas Industry %A Yang, Xiaojing %A Li, Zhihan %A Sun, Gege %A Li, Mengyue %A Beloucif, Meriem %Y Shterionov, Dimitar %Y Vanmassenhove, Eva %Y De Sisto, Mirella %Y Blain, Fred %Y Pourmostafa Roshan Sharami, Javad %Y Lepp, Lisa %Y Manna, Chiara %Y Rescigno, Argentina Anna %Y Karakanta, Alina %Y Rigouts Terryn, Ayla %Y Lardelli, Manuel %Y Resende, Natalia %Y Murgolo, Elena %Y Hackenbuchner, Janiça %Y Zaretskaya, Anna %Y Esplà-Gomis, Miquel %Y Etchegoyhen, Thierry %Y Gromann, Dagmar %Y Bawden, Rachel %Y Haddow, Barry %Y Szoc, Sara %Y Forcada, Mikel %Y Moniz, Helena %S Proceedings of the 26th Annual Conference of the European Association for Machine Translation (Volume 1) %D 2026 %8 June %I European Association for Machine Translation %C Tilburg, The Netherlands %@ 9789403901411 %F yang-etal-2026-lora %X Adapting large language models to specialized domains remains challenging due to the computational cost of full finetuning and the limited availability of domain-specific parallel data. We present a systematic framework for parameter-efficient domain adaptation using Low-Rank Adaptation (LoRA) geared towards efficient learning in low-resource scenarios. Our method combines data-scaling analysis, dual-track hyperparameter optimization, and competitive benchmarking. We evaluate our approach on the low-resource English–Norwegian petroleum translation domain using a distilled version of NLLB and parallel data from the Norwegian Petroleum Directorate. Our adapted model achieves 61.48 BLEU (+24.62 over the base model) and 0.9298 COMET, while updating \ensuremath<0.4% of parameters. Our results provide a reproducible and computationally efficient blueprint for domain adaptation in neural machine translation, particularly for specialized and resource-constrained domains. %U https://aclanthology.org/2026.eamt-1.25/ %P 385-398
Markdown (Informal)
[LoRA Fine-Tuning of English–Norwegian NMT for the Oil & Gas Industry](https://aclanthology.org/2026.eamt-1.25/) (Yang et al., EAMT 2026)
- LoRA Fine-Tuning of English–Norwegian NMT for the Oil & Gas Industry (Yang et al., EAMT 2026)
ACL
- Xiaojing Yang, Zhihan Li, Gege Sun, Mengyue Li, and Meriem Beloucif. 2026. LoRA Fine-Tuning of English–Norwegian NMT for the Oil & Gas Industry. In Proceedings of the 26th Annual Conference of the European Association for Machine Translation (Volume 1), pages 385–398, Tilburg, The Netherlands. European Association for Machine Translation.