# Fine-tuning NMT Models and LLMs for Specialised EN-ES Translation Using Aligned Corpora, Glossaries, and Synthetic Data: MULTITAN at WMT25 Terminology Shared Task

Lichao Zhu, Maria Zimina-Poirot, Cristian Valdez, Stéphane Patin

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Lichao Zhu, Maria Zimina-Poirot, Cristian Valdez, Stéphane Patin. Fine-tuning NMT Models and LLMs for Specialised EN-ES Translation Using Aligned Corpora, Glossaries, and Synthetic Data: MULTITAN at WMT25 Terminology Shared Task. The Tenth Workshop on Machine Translation (WMT), ACL, Nov 2025, Suzhou, China. pp.1284-1291. hal-05322834

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# Fine-tuning NMT Models and LLMs for Specialised EN-ES Translation Using Aligned Corpora, Glossaries, and Synthetic Data: MULTITAN at WMT25 Terminology Shared Task

Lichao Zhu

Maria Zimina-Poirot

Cristian Valdez

Stéphane Patin

ALTAE, Université Paris Cité

{lichao.zhu, maria.zimina-poirot, cristian.valdez, stephane.patin}@u-paris.fr

## Abstract

This paper presents a hybrid evaluation of terminology-aware English-to-Spanish machine translation systems developed for the WMT25 Terminology Shared Task, specifically targeting the Information Technology (IT) domain. Our objective was to improve terminology accuracy and overall translation quality and highlight the potential of specialised terminology-aware translation models for technical domains. We used different enhancement strategies for both neural machine translation (NMT) systems and large language models (LLMs). These strategies include fine-tuning with synthetic data, the use of in-domain parallel corpora, and hard constraint methods such as placeholder substitution and in-context glossary integration. The results demonstrate distinct lexical and stylistic profiles in the outputs of fine-tuned NMT systems and LLMs, as well as the complementary advantages of different terminology injection methods. Systems behave differently with and without a glossary, as demonstrated by experimental results. The NMT systems seem to be rather limited in adapting to special lexicons and resizing embeddings, which is the opposite of LLMs, which prefer structured instructions. Although our translation systems achieved their highest scores on the *NoTerm, Consistency* metrics, exceeding 81%, demonstrating their ability to produce stable and coherent translations of recurring terms and phrases in unconstrained settings, the precision of the terminology and overall quality of the translation could have been improved by additional training.

## 1 Introduction

Adaptation of the NMT model to a specific domain (Chu et al., 2017; Chu and Wang, 2018; Saunders, 2021) is a major concern in bilingual neural machine translation. Among the various methods that have been proposed to tackle domain adaptation,

two approaches are particularly relevant to the objective of this shared task: i) **Data approach** that involves selecting and filtering existing in-domain parallel segments (Moore and Lewis, 2010; Axelrod et al., 2011), or generating synthetic data. The latter is widely used in back-translation and in enhancing domain-specific data by reformulating or paraphrasing (Sennrich et al., 2016; Edunov et al., 2018); ii) **System approach** which aims to assign weights to segments close to the target domain (Wang et al., 2017). In recent years, research has increasingly focused on frugal domain adaptation strategies, emphasising the efficient use of limited resources and optimising training settings to address low-resource scenarios (Adams et al., 2022; Marashian et al., 2025). With the rapid development of LLMs for translation purposes in recent years, several methods for LLM domain adaptation have emerged, such as prompt engineering and context leaning (Zhang et al., 2023; Pourkamali and Sharifi, 2024; Yamada, 2023), constrained decoding to enforce terminology or special data format (Luca Beurer-Kellner, 2024; Bogoychev and Chen, 2023), Supervised Fine-Tuning (SFT) using reinforcement learning from human feedback (Ouyang et al., 2022), etc. Meanwhile, domain adaptation sheds light on the functioning, strengths, and weaknesses of LLM (Lu et al., 2025a), making these 'black boxes' more interpretable – an important objective of our participation in the shared task. In our contribution, we have chosen to use three strategies to fine-tune both NMT and LLM systems to ensure accurate translation of terms tagged as 'proper_terms' in the dev set:

* An open source NMT system was fine-tuned by employing placeholders for lexical-constrained decoding using additional aligned segments within the domain.

* An artificially augmented training data set
was created using a prompt system and used
to fine-tune a baseline on a commercial
model training server.

* Using aligned segments and a glossary, Low-Rank Adaptation was used to make minor changes to an LLM (Hu et al., 2021).

## 2 Data processing and augmentation

### 2.1 Data sources for domain-specific model specialisation

To achieve precise terminology and consistency in
fields like finance, IT and legal texts, it is essential
to use high-quality aligned corpora and domain-
specific glossaries to fine-tune NMT models and
LLMs for specialised machine translation. The
use of synthetic data generation methods can also
help to augment domain-specific corpora, enhanc-
ing models' abilities to manage specialised ter-
minology and contexts effectively. Prompt-based
generation, retrieval-augmented generation, self-
instruction, and reinforcement learning with feed-
back are some of the approaches available for syn-
thetic data generation (Lu et al., 2025b; Nad et al.,
2025). These methods can improve model perfor-
mance, data diversity, and adaptability to domain-
specific requirements by supplementing real train-
ing data with synthetic examples generated by
LLMs.

## 2.2 High-quality parallel corpus

To increase the size of the training data and
achieve a broader terminological coverage, we
used a high-quality parallel corpus in IT and
closely related fields: the European Union Intellec-
tual Property Office (EUIPO) Trademark and De-
sign Guidelines in the production, technology and
research domains, translated by professional trans-
lators from English into Spanish.<fnref n="1" /> This resource,
created by the European Language Resource Co-
ordination (ELRC), contains 16,439 translation
units; 386,472 tokens in English and 424,702 to-
kens in Spanish. After filtering based on length
control and the basic alignment quality of the seg-
ments, we obtained 6,359 parallel segments con-
taining 62,481 English tokens and 67,791 Spanish
tokens, representing an average of 910 words (60-
66 characters) per segment.

<figure><img src="image_2.png" /><figcaption>Figure 1: Parallel co-occurrences in the augmented data, measuring the frequency and specificity of lexical attractions.<fnref n="2" /></figcaption></figure>

<figure><img src="image_3.png" /><figcaption>Figure 2: Parallel co-occurrences in the development set of the shared task, measuring the frequency and specificity of lexical attractions.</figcaption></figure>

## 2.3 In-domain synthetic data

In our experiments, we used the dev data set (enes-
dev: 500 aligned segments) augmented via a sys-
tem of prompts on ChatGPT-4 (first to generate
an initial data set in English using task-specific
terminology) and on DeepSeek (to translate the
generated dataset into Spanish using the WMT
2025 dev set term correspondences). We used free
public versions of both platforms. Our objective
was not only to consider specific term correspon-
dences, but also to capture linguistic features of
the test set to create synthetic data reproducing
the dev set patterns. We created a synthetic data
set enes-AUG (1,746 aligned English-Spanish sen-
tence pairs, 10,021 tokens in English, 10,822 to-
kens in Spanish). The generated synthetic data set
has some recognisable characteristics in terms of
regularly repeated characteristic patterns (for ex-
ample, in Spanish: Usa las acciones en tu docu-
mento. Utilice los Servicios profesionales en su
documento. Utilice el Soporte SAP en su docu-
mento).

<footnote n="1">¹https://is.gd/LlPIYr</footnote>

<footnote n="2">²Calculated by iTrameur (textometrics tool): https://itrameur.clillac-arp.univ-paris-diderot.fr</footnote>
We analysed parallel co-occurrence networks (Zimina and Fleury, 2016) in sentences containing the same terms in the dev set and in the augmented data. It reveals that both data sets share key terminological traits (see Figures 2.3 and 2), supporting the suitability of AI-generated data for our experiment. However, the AI-generated data set lacks the uppercase usage typical of the enes-dev data. For example, the term "Source Document Category" appears in lowercase. Future experiments could include prompt instructions to better replicate capitalisation in terminology.

## 2.4 Glossary terms and augmented terminology

The enes-dev set contains some rather challenging (or even questionable) annotations of "proper" and "random terms", for example:

"en": "Source Document Category",
"es": "Tipo de documento de origen",
"proper_terms": {"source document category": "tipo de documento de origen"}, "random terms": {"Source": "Origen", "Document": "documento", "Category": "tipo"}.

If *Tipo de documento de origen* is a term, then its constituents, such as *documento* and *tipo*, are hardly random in specialised discourse, especially if we consider term variation, which involves the use of different forms to express the same or nearly the same concepts within a specialised domain (Daille, 2017). In our work, we considered that such occurrences are part of specialised discourses and their distributional properties (Wingfield and Connell, 2022) are reflected in the augmented data set. We also tried to take into account the fact that different multiword terms possibly contain common lexical items. In this respect, term variation is reflected in the augmented data set. For example, while the term "source document category" is translated by *tipo de documento de origen*, our synthetic data set also contains contexts, where "doc-ument type" is translated by *clase de documento*:

en: Save the **source document category**. > es: *Guarda el tipo de documento de origen.*

en: Save the **document type**. > es: *Guarda la clase de documento.*

# 3 Systems

## 3.1 NMT system

We investigated methods to impose lexical constraints on NMT systems that do not inherently support glossary use. One approach involves fine-tuning a generic NMT model with domain-specific synthetic data generated through LLMs and other generative AI tools. This synthetic data set incorporates targeted terminology to guide system translations, improving sentence level accuracy and ensuring consistent terminology usage. The fine-tuning process leverages data augmentation techniques to integrate specific term correspondences and improve translation coherence within specialised domains.

## 3.2 LLM

Recent research suggests that LLMs can be effectively tuned for MT using surprisingly small amounts of high-quality parallel data. Fine-tuning with LLMs (such as Llama-2 7B) can deliver strong performance when trained on as few as 32,219 parallel sentences (Lu et al., 2025a). This is in line with the hypothesis of "superficial alignment", which suggests that LLMs have already mastered their translation skills during pre-training, and fine-tuning concentrates on aligning the model with the specific task format.

# 4 Experimental settings

We used constrained machine translation with hard terminology control (EN-ES) to ensure that English terms were consistently predicted as their Spanish equivalents. We implemented our NMT system with Marian (Junczys-Dowmunt et al., 2018), mainly because it allowed constraint decoding with the additional_special_tokens parameter, and the models embeddings were resized accordingly. In the training data, we replaced English terms and their Spanish equivalents with *placeholders*: each pair of bilingual terms was replaced by the same placeholder. Consequently, English terms in the input were replaced by placeholders before decoding, and placeholders in the output were replaced by the equivalent Spanish terms after generation. Our system was fine-tuned with Seq2SeqTrainer with the following setting: training epochs: 5, learning rate: 1e-5, training batch size: 8, gradient accumulation steps: 2, max length: 128.
For the LLM model, we used EuroLLM-1.7B-Instruct (Martins et al., 2024) as a causal LM due to its relatively light weight for fine-tuning. For each pair of aligned segments (EN-ES), the system builds a prompt that injects the glossary before instruction as follows:

#### Instruction:

> "Translate the following text from English to Spanish using the provided glossary."
> "### Glossary (Information Technology):"
> "{glossary_items}"
> "### Text in English:"
> "{src}"
> "### Translation in Spanish:"
> "{tgt}"

The glossary was created using the English and Spanish lexicons tagged as "proper_term" elements in the dev set. After filtering and deduplication, we obtained 172 unique occurrences in English (not case-sensitive). These occurrences correspond to 221 pairs of English and Spanish terms. Training was set as follows: training batch size: 4, learning rate: 1e-4, weight decay: 0.01, lr scheduler type: linear, warmup steps: 100, training epochs: 1. We used parameter-efficient LoRA (Hu et al., 2021) adapters for light fine-tuning, using 8-bit loading to reduce GPU memory usage: task type: CAUSAL LM, r: 16, lora alpha: 32, lora dropout: 0.1. For both the Marian and EuroLLM fine-tuning processes, we used a small number of epochs and a small batch size to reduce training time and prevent over-fitting. The main purpose was to evaluate the efficiency of our training pipelines.

We also used an advanced commercial MT platform SYSTRAN Model Studio Lite<fnref n="3" /> to fine-tune a generic EN-ES baseline model. NMT was used to ensure accurate sentence-level translations. GenAI, including LLMs, was used to develop a synthetic data set that contains domain-specific terms to fine-tune the initial system and implement specific term correspondences, improving coherence regarding terminology specifications (see Section 2.3 on data augmentation techniques).

# 5 First results

## 5.1 Quantitative metrics

We investigated lexical characteristics and vocabulary usage in six translations produced by different models (see Table 1) and compared them using quantitative methods, such as correspondence analysis, vocabulary growth assessment, and characteristic elements computation (Lebart et al., 1998). The investigation revealed nuanced differences in the translations and highlighted the impact of glossary inclusion and placeholder handling on translation quality and style.

A corpus aggregated from six different translations contains a total of 35,250 tokens. On average, each translated text comprises approximately 5,875 tokens, with two notable exceptions: **EuroLLM_SEG_Glossary** (6,192 tokens) and **MTwithplaceholder** (5,400 tokens).

### 5.1.1 Vocabulary growth

The vocabulary growth curves, which reflect the natural increase in distinct words encountered, show that **EuroLLM_SEG_Glossary** consistently has the largest vocabulary size (see Figure 3). This suggests that it has the most diverse vocabulary of all the models tested. **Systran_SEG_AUG** exhibits somewhat lower vocabulary growth, very close to the reference translation, while **Marian_SEG** shows the slowest increase, suggesting a smaller or more limited vocabulary compared to the others.

### 5.1.2 Characteristic elements

The results of characteristic elements computation reveal the use of English lexical units in **MTwithplaceholder**, which do not correspond to common Spanish borrowings, highlighting the models lexical weaknesses. **EuroLLM_SEG_Glossary** distinctly exhibits a preference for informal address forms, favouring pronouns and verbs such as *puedes*, *selecciona*, and *tus* over their formal counterparts like *pueda* or *Selecciona*. It also underuses the term *plantilla*, substituting it with *modelo* or omitting it entirely, indicating stylistic or glossary-driven variations.

### 5.1.3 Correspondence analysis

Correspondence analysis provided a multidimensional visualisation of the lexical relationships

<footnote n="3">³https://modelstudio-lite.systran.net</footnote>

<footnote n="4">⁴Generated with iTrameur (textometrics tool): https://itrameur.clillac-arp.univ-paris-diderot.fr</footnote>
<figure><img src="image_4.png" /><figcaption>Figure 3: Comparison of vocabulary growth curves among submitted translations and the reference.⁴</figcaption></figure>

across the models (see Figure 4). The first factorial plane, which accounts for 49.9% of the total lexical variation, highlights the following oppositions:

* **(Pure) MT translations vs. (human) reference:** As expected, the results of the correspondence analysis highlight the differences between the reference translation and the translations produced by the submitted MT systems.

* **NMT vs. LLM:** Submitted MT translations are positioned along a continuum that differentiates between NMT and LLM characteristics. The **Systran Seg Aug** system, which relies on LLM-generated data, exhibits hybrid traits.

The second factorial plane (see Figure 4), which accounts for 26.7% of the total lexical variation, further confirms oppositions and proximities observed in the first plane, mapping a closer proximity between **Systran Seg Aug** and the reference translation compared to the other submitted translations:

* **Systran\_SEG\_AUG** shows some characteristics that approximate the **reference** translation.

### 5.1.4 Performance scores

Following this analysis, we conducted an evaluation using the reference translations provided by the organisers. Table 2 compares the performance

of pre-trained or generic models (Marian MT, EuroLLM, and Systran *Generic*) with that of fine-tuned models. Our analysis shows that although constrained decoding helps models better translate "proper terms", it reduces the overall translation performance of MT systems. This effect is particularly evident in Marian MT: although the fine-tuned model translated more proper terms and produced more formal equivalences, its overall translation quality decreases, with the COMET-DA score dropping from 0.86 (pre-trained) to 0.82 (fine-tuned). However, constrained decoding improves the term-matching precision of EuroLLM without heavily degrading its overall translation quality. For Systran, the score difference between the pre-trained and fine-tuned models is minimal.

## 5.2 Linguistic analysis

Evaluating fluency is difficult since most segments are decontextualised noun phrases, but there are examples where fluency differences between models are noticeable (see Table 3).

**Systran\_SEG\_AUG** tends to produce translations that are quite literal but accurately preserve the source content. The system generally yields the best results in terms of information transfer. The **Marian\_SEG** model sometimes omits segments. When this happens, there are negative effects on orthographic correctness, such as missing capital letters at the beginning of sentences.

<footnote n="5">⁵Generated with **Lexico 5**, lexicometrics software: https://lexi-co.com/L5Presentation.html</footnote>

<footnote n="6">⁶*: Submitted version.</footnote>
<figure><img src="image_5.png" /><figcaption>Figure 4: Factorial correspondence analysis of submitted translations. The first factorial plane, formed by axes 1 and 2, accounts for 49.9% of the variation. The second one, formed by axes 1 and 3, accounts for 26.7%.⁵</figcaption></figure>

<table><caption>Table 1: Quantitative characteristics of the translations generated by each of the models.</caption><thead><tr><th>System</th><th>Token</th><th>Form</th><th>Hapax</th><th>Fmax</th></tr></thead><tbody><tr><td>ES_SystranIT</td><td>6,014</td><td>1,519</td><td>834</td><td>653</td></tr><tr><td>ES_Systrangeneric</td><td>5,976</td><td>1,504</td><td>849</td><td>677</td></tr><tr><td>EuroLLM_SEG_GLOSSARY*6</td><td><strong>6,192</strong></td><td><strong>1,670</strong></td><td><strong>990</strong></td><td><strong>690</strong></td></tr><tr><td>MTwithplaceholder</td><td>5,400</td><td>1,592</td><td>973</td><td>440</td></tr><tr><td>Marian_SEG*</td><td>5,664</td><td>1,351</td><td>762</td><td>658</td></tr><tr><td>Systran_SEG_AUG*</td><td>6,004</td><td>1,524</td><td>866</td><td>684</td></tr><tr><td>Total</td><td>35,250</td><td>2,871</td><td>873</td><td>3,802</td></tr></tbody></table>

EuroLLM_SEG_GLOSSARY model occasionally leaves some English segments untranslated. This occurs rarely but more often than with other models. Some untranslated segments result from poor segmentation in the source text. There is no consistent correlation between the omission or retention of expected terms across models. For example, sometimes expected terms are omitted, sometimes other segments are omitted leaving only the expected term, and in some rare cases the source text remains unchanged.

In cases where EuroLLM_SEG_GLOSSARY leaves English segments untranslated (e.g., "On the To Be Billed"), it appears to stem from improper segmentation. For example, the segment "On the To Be Billed" that is left in the translation generated by EuroLLM_SEG_GLOSSARY is the result of poor segmentation of the original version. If the decision was to leave the tab name in English, it should have been rendered as "*en la pestaña To Be Billed*". EuroLLM_SEG_GLOSSARY sometimes introduces lexical creations or barbarisms, which can make understanding difficult.

In summary, Systran_SEG_AUG performs best in preserving source information. Marian_SEG suffers from segment omission causing orthographic errors. EuroLLM_SEG_GLOSSARY tends to leave

English untranslated more often and sometimes produces confusing lexical forms.

## 5.3 Interpretative insights

This combined quantitative and qualitative analysis shows that translation models vary in more than just lexical richness; they also differ in terms of stylistic choices and lexical specificity. There are significant differences in how models handle formality, lexical borrowing, and terminology consistency, reflecting variations in MT architecture and preprocessing (e.g. glossaries and placeholders). Correspondence analysis is a useful tool for visualising these differences, as it reduces complex lexical data into interpretable axes of variation. This enables more informed evaluations of MT output.

## 6 Conclusion and perspectives

Our contribution to the shared task reveals the strengths and challenges of NMT systems and LLMs in translation tasks. Although NMT systems perform well with small amounts of high-quality domain-specific training data, their performance can deteriorate under constrained decoding conditions. In contrast, LLMs can benefit from structured guidance, such as glossaries and clear instructions, which enhances their translation quality. These findings emphasise the complementary
<table><caption>Table 2: Term coverage and evaluation scores</caption><thead><tr><th>System</th><th>Total terms</th><th>Translated terms</th><th>Ratio</th><th>BLEU</th><th>chrF</th><th>COMET DA</th></tr></thead><tbody><tr><td>EuroLLM_SEG_GLOSSARY</td><td>538</td><td>204</td><td>37.9%</td><td>38.6</td><td>69.1</td><td>0.83</td></tr><tr><td>EuroLLM_1.6B_Pre-trained</td><td>538</td><td>199</td><td>37.0%</td><td>36.1</td><td>53.4</td><td>0.84</td></tr><tr><td>Marian_SEG</td><td>538</td><td><strong>254</strong></td><td><strong>47.2%</strong></td><td>48.1</td><td>73.2</td><td>0.82</td></tr><tr><td>Marian.Pre-trained</td><td>538</td><td>196</td><td>36.4%</td><td>45.0</td><td>58.1</td><td>0.86</td></tr><tr><td>Systran_SEG_AUG</td><td>538</td><td>222</td><td>41.3%</td><td>50.7</td><td><strong>75.7</strong></td><td><strong>0.88</strong></td></tr><tr><td>Systran_Generic</td><td>538</td><td>221</td><td>41.1%</td><td><strong>51.2</strong></td><td>73.2</td><td>0.88</td></tr></tbody></table>

<table><caption>Table 3: Translation comparisons</caption><thead><tr><th>ID</th><th>Source text</th><th>Systran_SEG_AUG</th><th>Marian_SEG</th><th>EuroLLM_SEG_GLOSSARY</th><th>Term</th></tr></thead><tbody><tr><td>1</td><td>On the To Be Billed Tab, select one or more items as required and choose Write Off.</td><td>En la ficha Para facturar, seleccione uno o más elementos según sea necesario y elija Cancelar.</td><td>En la pestaña Para ser facturado seleccione uno o más elementos como sea necesario y seleccione Escribir apartado.</td><td>En la pestaña On the To Be Billed, seleccione uno o más elementos según sea necesario y elija Deshacer.</td><td>Write off&>ignorar</td></tr><tr><td>2</td><td>On the To Be Billed Tab, select one or more items as required and choose Restrict Date.</td><td>En la ficha Para facturar, seleccione uno o más elementos según sea necesario y Restringir fecha.</td><td>En la pestaña Para ser facturado seleccione uno o más elementos como sea necesario y seleccione Limitar fecha.</td><td>En la pestaña On the To Be Billed, seleccione uno o más elementos según sea necesario y Restringir Date.</td><td>Tab&>pestaña</td></tr></tbody></table>

nature of the two approaches, suggesting that combining NMT's data-driven learning with LLMs' flexible, instruction-driven capabilities could result in more robust and effective translation solutions.

We also identified some limitations through our experimentation. None of our open-source systems translated all the segments from English to Spanish, with some segments always remaining untranslated. This is probably due to gaps in the lexicons of the training and test data or to forced decoding effects that prioritise and select the source token over the target token. In summary, a trade-off must be made between model complexity, performance, and training cost. While a 16B-parameter model could potentially avoid missing any translations, it is much more challenging to fine-tune such a model due to its substantial number of parameters, size, and the associated high training costs in terms of GPU memory, time, and environmental impact.

## 7 Acknowledgements

This research was funded by the 2024 MULTITAN-GML Research Equipment Grant (COPES-2024-12, Fonds d'intervention Recherche, Université Paris Cité) and used

PNS-UP scientific platform<fnref n="7" />.

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