Morocco

Tonogenesis—the historical process by which segmental contrasts evolve into lexical tone—has traditionally been studied through comparative reconstruction and acoustic phonetics. We introduce a computational approach that quantifies the functional role of pitch at different stages of this sound change by measuring how pitch manipulation affects automatic speech recognition (ASR) performance. Through analysis on the sensitivity to pitch-flattening from a set of closely related Tibetan languages, we find evidence of a tonogenesis continuum: atonal Amdo dialects tolerate pitch removal the most, while fully tonal Ü-Tsang varieties show severe degradation, and intermediate Kham dialects fall measurably between these extremes. These gradient effects demonstrate how ASR models implicitly learn the shifting functional load of pitch as languages transition from consonant-based to tone-based lexical contrasts. Our findings show that computational methods can capture fine-grained stages of sound change and suggest that traditional functional load metrics, based solely on minimal pairs, may overestimate pitch dependence in transitional systems where segmental and suprasegmental cues remain phonetically intertwined.

EACL 2026

The Tonogenesis Continuum in Tibetan: A Computational Investigation

pitch

tibetan

tonogenesis

language change

tone

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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. 

Small language models (SLMs) offer computationally efficient alternatives to large language models, yet their translation quality for low-resource languages (LRLs) remains severely limited. This work presents the first large-scale evaluation of SLMs across 200 languages, revealing systematic underperformance in LRLs and identifying key sources of linguistic disparity. We show that knowledge distillation from strong teacher models using predominantly monolingual LRL data substantially boosts SLM translation quality—often enabling 2B–3B models to match or surpass systems up to 70B parameters. Our study highlights three core findings: (1) a comprehensive benchmark exposing the limitations of SLMs on 200 languages; (2) evidence that LRL-focused distillation improves translation without inducing catastrophic forgetting, with full-parameter fine-tuning and decoder-only teachers outperforming LoRA and encoder–decoder approaches; and (3) consistent cross-lingual gains demonstrating the scalability and robustness of the method. These results establish an effective, low-cost pathway for improving LRL translation and provide practical guidance for deploying SLMs in truly low-resource settings.

Are Small Language Models the Silver Bullet to Low-Resource Languages Machine Translation?

Neural Machine Translation (NMT) performance degrades significantly in ultra-low resource settings, particularly for endangeredlanguages like Tao (Yami) which lack extensive parallel corpora. This study investigates strategies to bootstrap a Tao-Tagalog translation system using the NLLB-200 (600 million parameter) model under extremely limited supervision. We propose a multi-faceted approach combining domain-specific fine-tuning, synthetic data augmentation, and cross-lingual transfer learning. Specifically, we leverage the phylogenetic proximity of Ivatan, a related Batanic language, to pre-train the model, and utilize dictionary-based generation to construct synthetic conversational data. Our results demonstrate that transfer learning from Ivatan improves translation quality on in-domain religious texts, achieving a BLEU score of 34.85. Conversely, incorporating synthetic data enhances the model’s ability to generalize to conversational contexts, mitigating the domain bias often inherent in religious corpora. These findings highlight the effectiveness of exploiting linguistic typology and structured lexical resources to develop functional NMT systems for under-represented Austronesian languages.

Tao–Filipino Neural Machine Translation: Strategies for Ultra–Low-Resource Settings

In this paper, we propose a text filter designed to support multiple languages. The method simply aggregates vocabulary from a monolingual corpus and compares it against the input. Despite its simplicity, the approach proves highly effective in removing code-mixed text.When combined with existing language identification techniques, our method can enhance the purity of the corpus in the target language. Consequently, applying it to parallel corpora for machine translation has the potential to improve translation quality.Additionally, the proposed method supports the incremental addition of new languages without the need to retrain those already learned. This feature easily enables our method to be applied to low-resource languages.

Text Filter Based on Automatically Acquired Vocabularies for Multilingual Machine Translation

We present a comprehensive evaluation and extension of the LLM-Assisted Rule-Based Machine Translation (LLM-RBMT) paradigm, an approach that combines the strengths of rule-based methods and Large Language Models (LLMs) to support translation in no-resource settings. We present a robust new implementation (the Pipeline Translator) that generalizes the LLM-RBMT approach and enables flexible adaptation to novel constructions. We benchmark it against four alternatives (Builder, Instructions, RAG, and Fine-tuned translators) on a curated dataset of 150 English sentences, and compare them across translation quality and runtime. The Pipeline Translator consistently achieves the best overall performance. The LLM-RBMT methods (Pipeline and Builder) also offer an important advantage: they naturally align with evaluation strategies that prioritize grammaticality and semantic fidelity over surface-form overlap, which is critical for endangered languages where mistranslation carries high risk.

Comparing LLM-Based Translation Approaches for Extremely Low-Resource Languages

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.

Can LLMs Translate Italy’s Language Varieties?

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.

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Are Small Language Models the Silver Bullet to Low-Resource Languages Machine Translation?

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Next from EACL 2026

Are Small Language Models the Silver Bullet to Low-Resource Languages Machine Translation?

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Am I tone-deaf? Assessing pitch discrimination in 700,000 people

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