Morocco

We evaluate the capabilities of several small large language models (LLMs) to translate between Italian and six low-resource language varieties from Italy (Friulan, Ligurian, Lombard, Sicilian, Sardinian, and Venetian). Using recent benchmark datasets, such as FLORES+ and OLDI-Seed, we compare prompting and fine-tuning approaches for downstream translation, evaluated with CHRF scores. Our findings confirm that these LLMs struggle to translate into and from these low-resource language varieties. Pretraining and fine-tuning a small LLM did not yield improvements over a zero-shot baseline. These results underscore the need for further NLP research on Italy’s low-resource language varieties. As the digital divide continues to threaten the conservation of this diverse linguistic landscape, greater engagement with speaker communities to create better and more representative datasets is essential to boost the translation performance of current LLMs.

EACL 2026

Can LLMs Translate Italy’s Language Varieties?

languages of italy

low-resource

machine translation

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EMNLP 2023

EMNLP 2023 took place in Singapore from Dec 6th to Dec 10th, 2023.

workshop paper

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Welcome to the 19th Conference of the European Chapter of the Association for Computational Linguistics (EACL). EACL 2026 will be held in Rabat, Morocco, from March 24–29, 2026. 

Integrating domain-specific terminology into Machine Translation systems is a persistent challenge, particularly in low-resource and morphologically-rich scenarios where models lack the robustness to handle imposed constraints. This paper investigates the trade-off between static dictionary-based data augmentation and dynamic inference constraints (Constrained Beam Search). We evaluate these methods on two high-to-low resource language pairs: English-Maltese (Semitic) and English-Slovak (Slavic). Our experiments reveal a dichotomy: while dynamic constraints achieve near-perfect Terminology Insertion Rates (TIR), they drastically degrade translation quality (BLEU) in low-resource settings, breaking the fragile fluency of the model. Conversely, static augmentation improves terminology adherence on unseen terms in Maltese (4% $\rightarrow$ 19%), but fails in the context of a highly inflected language like Slovak. To resolve this conflict, we propose Hybrid Fallback Term Injections, a strategy that prioritizes the fluency of static models while using dynamic constraints as a safety net. This approach recovers up to 90% of missing terms while mitigating the quality degradation of pure constraint approaches, providing a viable solution for high-fidelity translation in data-scarce environments.

Balancing Fluency and Adherence: Hybrid Fallback Term Injection in Low-Resource Terminology Translation

Neural Machine Translation (NMT) models for low-resource languages suffer significant performance degradation under domain shift. We quantify this challenge using **Dhao**, an indigenous language of Eastern Indonesia with no digital footprint beyond the New Testament (NT). When applied to the unseen Old Testament (OT), a standard NMT model fine-tuned on the NT drops from an in-domain score of $36.17$ chrF++ to $27.11$ chrF++. To recover this loss, we introduce a **hybrid framework** where a fine-tuned NMT model generates an initial draft, which is then refined by a Large Language Model (LLM) using Retrieval-Augmented Generation (RAG). The final system achieves $35.21$ chrF++ ($+8.10$ recovery), effectively matching the original in-domain quality. Our analysis reveals that this performance is driven primarily by the **number of retrieved examples** rather than the choice of retrieval algorithm. Qualitative analysis confirms the LLM acts as a robust "safety net," repairing severe failures in zero-shot domains.

Context Volume Drives Performance: Tackling Domain Shift in Extremely Low-Resource Translation via RAG

Low-resource languages like Urdu suffer from limited high quality parallel data for machine translation. We introduce a curated English--Urdu corpus of 80,749 high-fidelity sentence pairs across 18 diverse domains, built via ethical collection, manual alignment, deduplication, and strict length-based filtering (AWCD $\leq$ 5). The corpus is converted into a bidirectional SFT dataset with bilingual (English/Urdu) instructions to enhance prompt-language robustness. Fine-tuning Llama-3.1-8B-Instruct (Llama-FT) and UrduLlama 1.1 (UrduLlama-FT) yields major gains over the baseline. sacreBLEU scores reach 24.65--25.24 (En$\to$Ur) and 76.14--77.97 (Ur$\to$En) for Llama-FT, with minimal sensitivity to prompt language. Blind human evaluation on 90 sentences per direction confirms substantial perceptual improvements. Results demonstrate the value of clean parallel data and bilingual instruction tuning, revealing complementary benefits of general SFT versus Urdu specific pretraining. This work provides a reproducible resource and pipeline to advance Urdu machine translation and similar low-resource languages.

Building and Evaluating a High Quality Parallel Corpus for English Urdu Low Resource Machine Translation

This paper presents a set of linguistic resources that describes Quechua verbs. We first present a dictionary of 1,444 fundamental Quechua verbs, associated with morpho-syntactic grammars to formalize their inflection and their derivations, that can be used to produce over 2,777,000 conjugated Quechua derived verbal forms. We aligned this list of Quechua verbal forms with the corresponding Spanish dictionary that contains 618,000 conjugated verbal forms, thus producing both a Spanish to Quechua and a Quechua to Spanish dictionary.

Semi-Automatic construction of a Quechua-Spanish dictionary

Machine translation for Indigenous and other low-resource languages is constrained by limited parallel data, orthographic variation, and evaluation instability for morphologically rich languages. In this work, we study Spanish–Aymara, Spanish–Guarani, and Spanish–Quechua translation in the context of the AmericasNLP benchmarks, focusing on data-centric improvements rather than architectural changes. We augment curated parallel corpora with forward-translated synthetic sentence pairs generated using a high-capacity multilingual translation model, while applying conservative, language-specific preprocessing tailored to each language. Training data is filtered using length-ratio constraints and deduplication, whereas official development sets are left unfiltered to ensure fair evaluation. We fine-tune a multilingual mBART model under curated-only and curated+synthetic settings and evaluate performance primarily using chrF++, which is better suited for agglutinative languages than BLEU. Across all three languages, synthetic data augmentation consistently improves chrF++, with the largest gains observed for Aymara and Guarani, while Quechua benefits primarily from deterministic orthographic normalization. Our analysis highlights both the effectiveness and the limitations of generic preprocessing for highly agglutinative languages, suggesting that data-centric augmentation and language-aware normalization are strong, reproducible baselines for low-resource Indigenous language machine translation.

Improving Indigenous Language Machine Translation with Synthetic Data and Language-Specific Preprocessing

Neural machine translation has achieved remarkable results for high-resource languages, yet language isolates – those with no demonstrated genetic relatives – remain severely underserved, as they cannot benefit from cross-lingual transfer with related languages. We present the first NMT system for Nivkh, a critically endangered language isolate spoken by fewer than 100 fluent speakers in the Russian Far East. Working with approximately 9.5k parallel sentences – expanded through fine-tuned LaBSE sentence alignment – we adapt NLLB-200 to Nivkh-Russian translation. Since Nivkh is absent from NLLB's language inventory, we investigate proxy language token selection, comparing six typologically diverse languages: Bashkir, Kazakh, Halh Mongolian, Turkish, Tajik, and French. We find that using any proxy substantially outperforms random token initialization (BLEU 18-19.02 vs. 15.44 for rus→niv), confirming the value of proxy-based transfer. However, the choice of which proxy has minimal impact, with all six achieving comparable results despite spanning four language families and two scripts. This suggests that for language isolates, practitioners can select any typologically reasonable proxy without significant performance penalty. We additionally present preliminary experiments on dialect-specific models for Amur and Sakhalin Nivkh. Our findings establish baseline results for future Nivkh NLP research and provide practical guidance for adapting multilingual models to other language isolates.

Adapting Multilingual NMT to Language Isolates: The Role of Proxy Language Selection and Dialect Handling for Nivkh

Machine translation (MT) evaluation is central in guiding researchers on how to improve a model's performance. Current automatic evaluation practices fail to provide reliable insights into the specific translation errors that occur, especially for low-resource languages. This paper introduces the Lux-MT-Test-Suite, enabling a linguistically motivated and fine-grained analysis of Luxembourgish–English (LB-EN) MT based on 896 test items covering 12 linguistic categories and 36 linguistic phenomena. We compare a baseline local LLM (Gemma 3), its fine-tuned counterpart (LuxMT), and a proprietary state-of-the-art LLM (GPT-5) to analyse what local LLMs learn through fine-tuning in a low-resource setting and to assess performance differences between local and proprietary systems. The findings identify specific performance gains through fine-tuning, minor degradations, a difference in translation strategies, performance gaps between local and proprietary models, and remaining challenges.

A Fine-Grained Linguistic Evaluation of Low-Resource Luxembourgish–English MT

Neural Machine Translation (NMT) systems rely heavily on explicit punctuation cues to resolve semantic ambiguities in a source sentence. Inputting user-generated sentences, which are likely to contain missing or incorrect punctuation, results in fluent but semantically disastrous translations. This work attempts to highlight and address the problem of punctuation robustness of NMT systems through an English-to-Marathi translation. First, we introduce \textbf{\textit{Vir\=am}}, a human-curated diagnostic benchmark of 54 punctuation-ambiguous English-Marathi sentence pairs to stress-test existing NMT systems. Second, we evaluate two simple remediation strategies: cascade-based \textit{restore-then-translate} and \textit{direct fine-tuning}. Our experimental results and analysis demonstrate that both strategies yield substantial NMT performance improvements. Furthermore, we find that current Large Language Models (LLMs) exhibit relatively poorer robustness in translating such sentences than these task-specific strategies, thus necessitating further research in this area. The code and dataset are available at https://github.com/KaustubhShejole/Viram_Marathi.

Assessing and Improving Punctuation Robustness in English-Marathi Machine Translation

Large Language Models (LLMs) have achieved strong performance across many downstream tasks, yet their effectiveness in extremely low-resource machine translation remains limited. Standard adaptation techniques typically rely on large-scale parallel data or extensive fine-tuning, which are infeasible for the long tail of underrepresented languages. In this work, we investigate a more constrained question: in data-scarce settings, to what extent can linguistically similar pivot languages and few-shot demonstrations provide useful guidance for on-the-fly adaptation in LLMs? We study a data-efficient experimental setup that combines linguistically related pivot languages with few-shot in-context examples, without any parameter updates, and evaluate translation behavior under controlled conditions. Our analysis shows that while pivot-based prompting can yield improvements in certain configurations, particularly in settings where the target language is less well represented in the model’s vocabulary, the gains are often modest and sensitive to few shot example construction. For closely related or better represented varieties, we observe diminishing or inconsistent gains. Broadly, our findings provide empirical guidance on how and when inference-time prompting and pivot-based examples can be used as a lightweight alternative to fine-tuning in low-resource translation settings.

Can Linguistically Related Languages Guide LLM Translation in Low-Resource Settings?

We explore machine translation for five Turkic language pairs: Russian-Bashkir, Russian-Kazakh, Russian-Kyrgyz, English-Tatar, English-Chuvash. Fine-tuning nllb-200-distilled-600M with LoRA on synthetic data achieved chrF++ 49.71 for Kazakh and 46.94 for Bashkir. Prompting DeepSeek-V3.2 with retrieved similar examples achieved chrF++ 39.47 for Chuvash. For Tatar, zero-shot or retrieval-based approaches achieved chrF++ 41.6, while for Kyrgyz the zero-shot approach reached 45.6. We release the dataset and the obtained weights.

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Balancing Fluency and Adherence: Hybrid Fallback Term Injection in Low-Resource Terminology Translation

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Language Varieties of Italy: Technology Challenges and Opportunities

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