United States

Low-resource machine translation (MT) presents a diversity of community needs and application challenges that remain poorly understood. To complement surveys and focus groups, which tend to rely on small samples of respondents, we propose an observational study on actual usage patterns of a specialized MT service for the Tetun language, which is the lingua franca in Timor-Leste. Our analysis of 100,000 translation requests reveals patterns that challenge assumptions based on existing corpora. We find that users, many of them students on mobile devices, typically translate text from a high-resource language into Tetun across diverse domains including science, healthcare, and daily life. This contrasts sharply with available Tetun corpora, which are dominated by news articles covering government and social issues.Our results suggest that MT systems for institutionalized minority languages like Tetun should prioritize accuracy on domains relevant to educational contexts, in the high-resource to low-resource direction. More broadly, this study demonstrates how observational analysis can inform low-resource language technology development, by grounding research in practical community needs.

NAACL 2025

Low-resource Machine Translation: what for? who for? An observational study on a dedicated Tetun language translation service

user-centered

low-resource

machine translation

### Welcome to IJCNLP-AACL 2025! 
 It is a great honor to host this joint conference in Mumbai, India, from December 20 to 24, 2025. The joint conferences of IJCNLP and AACL are organized with alternating leadership in the Asia-Pacific region. The event is run by the Asian Federation of Natural Language Processing (AFNLP) in odd years, and by AACL in even years, while it is organized solely by ACL when the annual ACL meeting is held in the region. This year, the conference is primarily organized by AFNLP. 
*Kentaro Inui
MBZUAI, UAE
General Chair, IJCNLP-AACL 2025* 
Read full message and download the Conference Handbook [**here**](https://drive.google.com/file/d/1UTwxkAqSqI-GAoJC3wE1zZt5VP1Y8GX0/view?usp=sharing).

IJCNLP-AACL 2025

The 14th IJCNLP & 4th AACL will be held in Mumbai, India from December 20th to December 24th, 2025.

workshop paper

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

Subword tokenization, where text is represented in an intermediate form between full words and characters, is ubiquitous in modern NLP due to its ability to represent any input sentence with a small vocabulary. However for Korean, where there are 11,172 base characters (*syllables*) in its alphabet, it is difficult to have a vocabulary large enough to succinctly encode text while fitting within parameter-budget constraints. This motivates us to explore an alternative representation for Korean which relies on the decompositional nature of Korean syllables: a syllable can be uniquely decomposed into a sequence of two or three subcharacters (*jamo*), of which there are only 68.Using jamo as the basis for subword tokenization (e.g., byte-pair encoding) leads to shorter tokenized sequences with fewer vocabulary parameters, exposes the model to sub-syllable-level morphological information, and increases the amount of augmentation gained from subword regularization. We evaluate jamo-level subword tokenization on several Korean translation tasks and find that jamo-level subword models consistently outperform syllable- and byte-level models in low-resource and restricted-vocabulary settings.

Jamo-Level Subword Tokenization in Low-Resource Korean Machine Translation

We introduce ModeLing, a novel benchmark of Linguistics Olympiad-style puzzles which tests few-shot reasoning in AI systems. Solving these puzzles necessitates inferring aspects of a language's grammatical structure from a small number of examples. Such puzzles provide a natural testbed for language models, as they require compositional generalization and few-shot inductive reasoning. Consisting solely of new puzzles written specifically for this work, ModeLing has no risk of appearing in the training data of existing AI systems: this ameliorates the risk of data leakage, a potential confounder for many prior evaluations of reasoning. Evaluating several large open source language models and GPT on our benchmark, we observe non-negligible accuracy, demonstrating few-shot emergent reasoning ability which cannot merely be attributed to shallow memorization. However, imperfect model performance suggests that ModeLing can be used to measure further progress in linguistic reasoning.

ModeLing: A Novel Dataset for Testing Linguistic Reasoning in Language Models

Domain adaptation in Neural Machine Translation (NMT) is commonly achieved through fine-tuning, but this approach becomes inefficient as the number of domains increases. Knowledge distillation (KD) provides a scalable alternative by training a compact model on distilled data from a larger model. However, we hypothesize that vanilla sequence-level KD primarily distills the decoder while neglecting encoder knowledge, leading to suboptimal knowledge transfer and limiting its effectiveness in low-resource settings, where both data and computational resources are constrained. To address this, we propose an improved sequence-level KD method that enhances encoder knowledge transfer through a cosine-based alignment loss. Our approach first trains a large model on a mixed-domain dataset and generates a Distilled Mixed Dataset (DMD). A small model is then trained on this dataset via sequence-level KD with encoder alignment. Experiments in a low-resource setting validate our hypothesis, demonstrating that our approach outperforms vanilla sequence-level KD, improves generalization to out-of-domain data, and facilitates efficient domain adaptation while reducing model size and computational cost.

Encoder-Aware Sequence-Level Knowledge Distillation for Low-Resource Neural Machine Translation

We introduce a new dataset for machine translation of Guinea-Bissau Creole (Kiriol), comprising around 40 thousand parallel sentences to English and Portuguese. This dataset is made up of predominantly religious data (from the Bible and texts from the Jehovah's Witnesses), but also a small amount of general domain data (from a dictionary). This mirrors the typical resource availability of many low resource languages, and we train a number of transformer-based models to investigate how to improve domain transfer from religious data to a more general domain. We find that adding even 300 sentences from the target domain when training substantially improves the translation performance, highlighting the importance and need for data collection for low-resource languages, even on a small-scale. We additionally find that Portuguese-to-Kiriol translation models perform better on average than other language pairs and directions, and investigate how this is related to the morphological complexity of the languages involved and the degree of lexical overlap between creoles and their lexifier languages. Overall, we hope our work will stimulate research into Kiriol, and into the translation of creole languages in general.

Limitations of Religious Data and the Importance of the Target Domain: Towards Machine Translation for Guinea-Bissau Creole

Machine translation (MT) of social media text presents unique challenges due to its informal nature, linguistic variations, and rapid evolution of language trends. In this paper, we propose a human-translated English dataset to Arabic, Italian, and Spanish, and a human-translated Arabic dataset to Modern Standard Arabic (MSA) and English. We also perform a comprehensive analysis of three publicly accessible MT models using human translations as a reference. We investigate the impact of social media informality on translation quality by translating the MSA version of the text and comparing BLEU and METEOR scores with the direct translation of the original social media posts. Our findings reveal that MarianMT provides the closest translations to human for Italian and Spanish among the three models, with METEOR scores of 0.583 and 0.640, respectively, while Google Translate provides the closest translations for Arabic, with a METEOR score of 0.354. By comparing the translation of the original social media posts with the MSA version, we confirm that the informality of social media text significantly impacts translation quality, with an increase of 12 percentage points in METEOR scores over the original posts. Additionally, we investigate inter-model alignment and the degree to which the output of these MT models align.

Comparative Evaluation of Machine Translation Models Using Human-Translated Social Media Posts as References: Human-Translated Datasets

In this study, we propose a novel paradigm for multi-modal low resource language dataset generation that eliminates dependency on existing parallel multi-modal datasets. Leveraging advances in large image-generation models, we introduce a systematic pipeline that transforms text-only parallel corpora into rich multi-modal translation datasets. We then validate the generated content through human evaluation. We design and implement a new MMT model framework suitable for our new generated dataset. The model contains a verification mechanism with a large language model to ensure consistency between visual content and textual translations. Experimental results across four African low-resource languages with less than 10k training corpus demonstrate significant improvements over NLLB baselines, with average gains of up to 9.8% in BLEU score and 4.3% in METEOR score. Our method shows particular effectiveness in correctly translating concrete objects and contextual elements, suggesting its potential for improving low-resource machine translation through visual grounding.

From Text to Multi-Modal: Advancing Low-Resource-Language Translation through Synthetic Data Generation and Cross-Modal Alignments

Current state-of-the-art large language models (LLMs) like GPT-4 perform exceptionally well in language translation tasks for high-resource languages, such as English, but often lack high accuracy results for low-resource African languages such as Igbo and Nigerian Pidgin, two native languages in Nigeria. This study addresses the need for Artificial Intelligence (AI) linguistic diversity by creating benchmark datasets for Igbo-English and Nigerian Pidgin-English language translation tasks. The dataset developed is curated from reputable online sources and meticulously annotated by crowd-sourced native-speaking human annotators. Using the datasets, we evaluate the translation abilities of GPT-based models alongside other state-of-the-art translation models specifically designed for low-resource languages. Our results demonstrate that current state-of-the-art models outperform GPT-based models in translation tasks. In addition, these datasets can significantly enhance LLM performance in these translation tasks, marking a step toward reducing linguistic bias and promoting more inclusive AI models.

Fostering Digital Inclusion for Low-Resource Nigerian Languages: A Case Study of Igbo and Nigerian Pidgin

Despite advances in the multilingual capabilities of Large Language Models (LLMs) across diverse tasks, English remains the dominant language for LLM research and development. So, when working with a different language, this has led to the widespread practice of pre-translation, i.e., translating the task prompt into English before inference. Selective pre-translation, a more surgical approach, focuses on translating specific prompt components. However, its current use is sporagic and lacks a systematic research foundation. Consequently, the optimal pre-translation strategy for various multilingual settings and tasks remains unclear. In this work, we aim to uncover the optimal setup for pre-translation by systematically assessing its use. Specifically, we view the prompt as a modular entity, composed of four functional parts: instruction, context, examples, and output, either of which could be translated or not. We evaluate pre-translation strategies across 35 languages covering both low and high-resource languages, on various tasks including Question Answering (QA), Natural Language Inference (NLI), Named Entity Recognition (NER), and Abstractive Summarization. Our experiments show the impact of factors as similarity to English, translation quality and the size of pre-trained data, on the model performance with pre-translation. We suggest practical guidelines for choosing optimal strategies in various multilingual settings

Beyond English: The Impact of Prompt Translation Strategies across Languages and Tasks in Multilingual LLMs

The diversity and complexity of Indic languages present unique challenges for natural language processing (NLP) tasks, particularly in the domain of question answering (QA).To address these challenges, this paper explores the application of State Space Models (SSMs) to build efficient and contextually aware QA systems tailored for Indic languages. SSMs are particularly suited for this task due to their ability to model long-term and short-term dependencies in sequential data, making them well-equipped to handle the rich morphology, complex syntax, and contextual intricacies characteristic of Indian languages. We evaluated multiple SSM architectures across diverse datasets representing various Indic languages and conducted a comparative analysis of their performance. Our results demonstrate that these models effectively capture linguistic subtleties, leading to significant improvements in question interpretation, context alignment, and answer generation. This work represents the first application of SSMs to question answering tasks in Indic languages, establishing a foundational benchmark for future research in this domain. Furthermore, we propose enhancements to existing SSM frameworks, optimizing their applicability to low-resource settings and multilingual scenarios prevalent in Indic languages.

Multilingual State Space Models for Structured Question Answering in Indic Languages

Parallel corpora play an important role in training machine translation (MT) models, particularly for low-resource languages where high-quality bilingual data is scarce. This review provides a comprehensive overview of available parallel corpora for Indic languages, which span diverse linguistic families, scripts, and regional variations. We categorize these corpora into text-to-text, code-switched, and various categories of multimodal datasets, highlighting their significance in the development of robust multilingual MT systems. Beyond resource enumeration, we critically examine the challenges faced in corpus creation, including linguistic diversity, script variation, data scarcity, and the prevalence of informal textual content. We also discuss and evaluate these corpora in various terms such as alignment quality and domain representativeness. Furthermore, we address open challenges such as data imbalance across Indic languages, the trade-off between quality and quantity, and the impact of noisy, informal, and dialectal data on MT performance. Finally, we outline future directions, including leveraging cross-lingual transfer learning, expanding multilingual datasets, and integrating multimodal resources to enhance translation quality. To the best of our knowledge, this paper presents the first comprehensive review of parallel corpora specifically tailored for low-resource Indic languages in the context of machine translation.

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