Morocco

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.

EACL 2026

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

low resource machine translation

### Welcome to 2025 Annual Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics

Welcome to the 2025 meeting of the Nations of the Americas Chapter of the Association for Computational Linguistics! I am proud to help organize the first NAACL conference to carry the new name of our organization, one that emphasizes inclusion for all of the Americas. I am also pleased to welcome you to Albuquerque, New Mexico, a state whose unique blend of cultural influences will make for an excellent backdrop for NAACL 2025, especially with this year’s special theme on NLP in a Multicultural World. 
**[Continue reading...](https://drive.google.com/file/d/1jX-qGhqVSZZCIrAnJaz798pflu5Irrdn/view?usp=sharing)**

*- Colin Cherry, Google, NAACL 2025 General Chair* 

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NAACL 2025

Welcome to 2025 Annual Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics

workshop paper

#### *Message from the General Chair, Aline Villavicencio*
I’m delighted and honoured to welcome you to the 19th Conference of the European Chapter of the Association for Computational Linguistics (EACL 2026), taking place in the beautiful city of Rabat, in Morocco, in March 24-29, 2026. EACL is the flagship European conference of the Association and EACL 2026 proudly continues our field’s tradition of excellence in scholarship, innovation, and inclusivity. I am deeply grateful to the many volunteers whose dedication, generosity, and tireless efforts have made this conference possible.
For the first time EACL is being hosted in the African continent. This is an important milestone for our community, and we are grateful to our Moroccan hosts for enabling this historic moment by bringing this edition of EACL to Rabat. We are also delighted that the Second Arabic NLP School is co-located with EACL. We hope attendees enjoy this wonderful opportunity to strengthen ties with the Computational Linguistics communities across the African continent. *[Read full message](https://drive.google.com/file/d/14NlmHvwM6fPJuMmOvVh7K0vtQbEyv3SZ/view?usp=sharing)* 

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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. 

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.

No One-Size-Fits-All: Building Systems For Translation to Bashkir, Kazakh, Kyrgyz, Tatar and Chuvash Using Synthetic And Original Data

This paper describes the submission of Team DevLake for the LoResMT 2026 Shared Task on Russian-Bashkir machine translation. We conducted a comprehensive comparative study of three distinct neural architectures: NLLB-200 (1.3B), M2M-100 (418M), and MarianMT (77M). To overcome hardware constraints, we employed parameter-efficient fine-tuning techniques (QLoRA) and extensive data filtering using a domain-specific BERT-based classifier. Our experiments demonstrate that the presence of the target language (Bashkir) in the model's pre-training data is the decisive factor for performance. Our best system, a fine-tuned NLLB-200-1.3B model augmented with exact match retrieval, achieved a CHRF++ score of 52.67. We also report on negative results with custom tokenization for smaller models, providing insights into the limitations of vocabulary adaptation without extensive pre-training.

DevLake at LoResMT 2026: The Impact of Pre-training and Model Scale on Russian-Bashkir Low-Resource Translation

This paper describes a submission to the LoResMT 2026 Shared Task for the Russian-Kazakh, Russian-Bashkir, and English-Chuvash tracks. The primary approach involves parameter-efficient fine-tuning (LoRA) of the Tencent HY-MT1.5-7B multilingual model. For the Russian-Kazakh and Russian-Bashkir pairs, LoRA adaptation was employed to correct the model's default Arabic script output to Cyrillic. For the extremely low-resource English-Chuvash pair, two strategies were compared: mixed training on authentic English-Chuvash and Russian-Chuvash data versus training exclusively on a synthetic English-Chuvash corpus created via pivoting through Russian. Baseline systems included NLLB 1.3B (distilled) for Russian-Kazakh and Russian-Bashkir, and Gemma 2 3B for English-Chuvash. Results demonstrate that adapting a strong multilingual backbone with LoRA yields significant improvements over baselines while successfully addressing script mismatch challenges. Code for training and inference is released at: https://github.com/defdet/low-resource-langs-mt-adapt

Script Correction and Synthetic Pivoting: Adapting Tencent HY-MT for Low-Resource Turkic Translation

We describe our submission to the Turkic languages translation challenge at LoResMT 2026, which focuses on translation from Russian into Kyrgyz. Our approach leverages parallel data, synthetic translations, a comprehensive filtering pipeline and a four-stage curriculum learning strategy. We compare our system with contemporary baselines and present the model that achieves a chrF++ score of 49.1 and takes first place in the competition.

Data-Centric Approach at the LoResMT 2026 Turkic Translation Challenge: Russian-Kyrgyz

The challenges of building speech-to-text translation (ST) systems (e.g., a relative lack of parallel speech–text data and robustness to noise in audio) are exacerbated for low-resource language pairs. In this work, we seek to improve low-resource ST by building on previous studies that regularize ST training with the connectionist temporal classification (CTC) loss. By systematically evaluating a diverse range of linguistic annotations as CTC labels across multiple auxiliary loss configurations, we improve speech translation systems for both low- and high-resource settings. These improvements over both a standard end-to-end ST system and a speech LLM indicate a need for continued research on regularizing speech representations in ST.

CTC Regularization for Low-Resource Speech-to-Text Translation

Noun compounds are generally considered an open challenge for NLP systems, given to the difficulty of interpreting the implicit semantic relation between modifier and head, although the advent of Large Language Models (LLMs) recently led to remarkable performance leaps. However, most evaluations have been carried out on English benchmarks.In our work, we test LLMs on compound semantics understanding in Chinese, adopting two different evaluation scenarios: an extrinsic evaluation in a Natural Language Inference task, and an intrinsic evaluation in which models are directly asked to predict the semantic relation linking the two constituents.Our results show that the bigger and more recent LLMs are able to surpass supervised baselines in the inference task, especially when tested under the few-shot setting. In the more challenging task of selecting the correct interpretation of the compounds out of a fine-grained typology of semantic relations between head and modifier, the best Chinese LLM (Qwen-plus) manages to select the correct option in about one third of the cases.

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

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UCSP Submission to the AmericasNLP 2025 Shared Task