United States

The term &quot;low-resource&quot; is used pervasively in NLP but communicates almost nothing precise. We propose RAN (Resource Abundance Notation), a compact, multi-dimensional notation for quantifying a language&#39;s NLP resource profile. A RAN score is written as S/M/L_1-B_1/L_2-B_2/..., where S = floor(log10(speakers)), M = floor(log10(monolingual sentences)), and each L_i-B_i pair records a bilingual partner and floor(log10(parallel sentences)). Values derive from canonical sources: Wikidata for speakers, OSCAR 23.01 for monolingual corpora, and (where available) OPUS for parallel corpora. We score 20 typologically diverse languages and correlate each profile against published benchmarks for three tasks: machine translation (MT, via NLLB-200 chrF++), named entity recognition (NER, via XTREME XLM-R WikiANN F1), and part-of-speech tagging (POS, via XTREME XLM-R UD accuracy). The RAN components carry complementary information: a linear model using all three explains 52% of MT variance, 76% of NER variance, and 72% of POS variance. Among single predictors, B_max (the largest bilingual corpus, regardless of partner) is strongest for the cross-lingual transfer tasks (NER, POS), while M and B_en are strongest for MT. RAN is designed first as a communication tool, not a predictive model.

ACL 2026

RAN: Resource Abundance Notation for Languages in NLP

resource notation

language resources

low-resource nlp

## Welcome to EMNLP 2022!
I am delighted to welcome you to EMNLP 2022! I believe this conference has been complicated beyond any precedent. Over the past year, it’s been thrilling to see the organization team approach each new puzzle with creativity and enthusiasm. We hope that those participating in Abu Dhabi as well as those joining remotely will leave the conference feeling newly inspired by the program and newly connected to our ever-growing community. Following EMNLP 2021 and major NLP conferences since, EMNLP 2022 is “hybrid,” serving both virtual and in-person participants.

Our key innovations for EMNLP 2022 include:

* EMNLP 2022 is “hybrid” in a second sense, as well: we allowed both direct and rolling review paper submissions, building on the pilot experiment of EMNLP 2021, which considered a small number of ARR submissions. 
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EMNLP 2022

Welcome!
EMNLP 2022 will take place in Abu Dhabi from December 7th to December 11th, 2022. And it will be held in hybrid mode, both online and offline.

The term "low-resource" is used pervasively in NLP but communicates almost nothing precise. We propose RAN (Resource Abundance Notation), a compact, multi-dimensional notation for quantifying a language's NLP resource profile. A RAN score is written as S/M/L_1-B_1/L_2-B_2/..., where S = floor(log10(speakers)), M = floor(log10(monolingual sentences)), and each L_i-B_i pair records a bilingual partner and floor(log10(parallel sentences)). Values derive from canonical sources: Wikidata for speakers, OSCAR 23.01 for monolingual corpora, and (where available) OPUS for parallel corpora. We score 20 typologically diverse languages and correlate each profile against published benchmarks for three tasks: machine translation (MT, via NLLB-200 chrF++), named entity recognition (NER, via XTREME XLM-R WikiANN F1), and part-of-speech tagging (POS, via XTREME XLM-R UD accuracy). The RAN components carry complementary information: a linear model using all three explains 52% of MT variance, 76% of NER variance, and 72% of POS variance. Among single predictors, B_max (the largest bilingual corpus, regardless of partner) is strongest for the cross-lingual transfer tasks (NER, POS), while M and B_en are strongest for MT. RAN is designed first as a communication tool, not a predictive model.

workshop paper

It is my great pleasure and honor to welcome you to the 64th Annual Meeting of the Association for Computational Linguistics (ACL 2026), held in San Diego, California, USA, July 2–7, 2026. ACL 2026 continues our fields tradition of excellence in scholarship, innovation, and inclusivity, and I am deeply grateful to the many volunteers who have worked tirelessly to bring this event to life.

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The 64th Annual Meeting of the Association for Computational Linguistics (ACL 2026) will take place in San Diego, California, from July 2 - 7, 2026.

For Mapudungun arn→es translation, morphology-aware tokenization can substitute for a 5× increase in model parameters. We fine-tune three sizes of Meta's NLLB-200 on Mapudungun–Spanish translation across eight tokenization strategies, including our novel Morfessor-VC method, which
constrains Morfessor morpheme segmentation to tokens already present in NLLB's pretrained
vocabulary. Our 600M Morfessor-VC model is competitive with our own fine-tuned 3.3B Standard BPE model on arn→es (43.2 vs. 42.9 chrF++, ∆ = +0.3, p = 0.039, 95% CI [0.02, 0.60]) while using five times fewer parameters, and all fine-tuned conditions surpass frontier LLMs by over 27 chrF++. Mapudungun is an indigenous polysynthetic language spoken by 200,000+ Mapuche people in Chile and Argentina, absent from NLLB-200 and not supported by major commercial MT providers; prior work predates large-scale multilingual models and does not address the tokenization challenges posed by its agglutinative
morphology. These results establish new state-of-the-art baselines for Mapudungun MT and provide a practical foundation for community language tools in pedagogy, social media, and language revitalization.

Bringing Mapudungun into the Modern MT Ecosystem: Morphology-Aware Tokenization for NLLB-200 Fine-Tuning

Qom, a language of the Guaycuruan family, is a low-resource language for NLP and speech processing. We present the first parallel Qom–Spanish corpus in a computationally usable format, comprising 33,392 parallel segments, totaling 1,469,905 Qom tokens and 891,344 Spanish tokens. A subset of 2,943 segments excludes Bible-derived content. It includes alignments at different levels: sentences, sentence fragments, and paragraphs, and is compiled from multiple sources, both previously available and newly collected. 
We also present bidirectional neural machine translation baselines based on NLLB-200, achieving competitive performance in both translation directions on the full dataset, and lower performance on the non-Bible subset. An ablation study shows that training exclusively on biblical data reduces performance on non-biblical text, highlighting the importance of domain diversity in low-resource machine translation.

QomL'aqtaqa: A Qom–Spanish Parallel Corpus for Natural Language Processing with Machine Translation Evaluation

Publicly available spoken language identification (LID) systems provide sparse and inconsistent coverage of indigenous languages of the Americas and languages of the Pacific Islands. No system on HuggingFace covers Central Alaskan Yup'ik except the largest variant of Meta's MMS-LID family, and only three MMS-LID variants cover Samoan, while Whisper and VoxLingua107-based models lack both despite including other Polynesian languages. We describe an ongoing effort to build a coarse-labeled LID dataset for Yup'ik and Samoan from US public broadcast archives, benchmark publicly available LID systems on it, and train a simple MLP classifier on frozen wav2vec~2.0 representations as a prototype. We report preliminary corpus statistics, off-the-shelf model performance, and prototype results. Guided by the distinctive phonological typology of the target languages, we outline a phonologically-informed fine-tuning direction as future work.

Toward a Coarse-Labeled Spoken Language Identification Dataset for Central Alaskan Yup'ik and Samoan from US Broadcast Archives

This system description presents a Wixarika image captioning system for the AmericasNLP 2026 Shared Task on Cultural Image Captioning for Indigenous Languages. The system uses Spanish as a pivot language, combining CLIP-based image retrieval, Qwen3-VL Spanish caption generation, the official Sheffield-compatible Spanish-to-Wixarika MT model, and character n-gram language-model reranking. We report local 5-fold development results, official test results, error analysis, and implementation details for reproducibility.

6fanle Submission to the AmericasNLP 2026 Shared Task on Wixarika Image Captioning

Culturally grounded image captioning for under-resourced Indigenous languages is challenging due to severe data scarcity and the need to describe culturally specific visual content. This paper describes our submission to the AmericasNLP 2026 shared task, where we evaluate two architectural paradigms for caption generation across Bribri, Guaraní, Yucatec Maya, Wixárika, and Orizaba Nahuatl. First, we implement a cascaded system that combines a large vision-language model with a machine translation pipeline, showing that culturally contextualized, persona-based prompting improves over the official baseline in most comparable settings. Second, we develop a direct, end-to-end Single-stage approach by adapting PaliGemma 2 using LoRA fine-tuning, continued pre-training, and multilingual joint training. Our single-stage experiments show that, despite severe domain mismatch and reliance on synthetic training data, multilingual training and continued pre-training improve automatic chrF++ relative to single-language LoRA fine-tuning in some settings. Overall, cascaded pipelines remain the strongest among the evaluated approaches under current data constraints, while single-stage models remain a promising but currently data-limited path toward direct Indigenous-language image captioning.

Culturally Grounded Image Captioning in Indigenous Languages with Vision-Language Models: Cascaded and Single-Stage Approaches

We describe the USP system for the AmericasNLP 2026 Shared Task on Culturally Relevant Image Captioning for Indigenous Languages, covering Guaraní (grn), Maya Yucateco (yua), Nahuatl (nah), Wixárika (hch), and Bribri (bzd). We propose a two-stage cascade: Qwen3-VL-8B-Instruct (Bai et al., 2025) generates Spanish captions via language-specific cultural prompts; language-specific fine-tuned NLLB-200-distilled-600M (NLLB Team et al., 2022) models then translate them into each target language. We train on AmericasNLP 2023 data (Ebrahimi et al., 2023) augmented with public parallel corpora. Our system achieves competitive results, including 3rd place in Guaraní human evaluation (2.41/5.0) and 5th in Bribri (1.09/5.0) among 8 teams. We also report that NLLB-200-distilled-600M silently lacks vocabulary entries for Bribri and Maya Yucateco, producing English output without error.

USP at AmericasNLP 2026 Shared Task: Culturally-Aware Image Captioning for Indigenous Languages via Vision-Language Models and Fine-Tuned Neural Machine Translation

Multilingual large language models (LLMs) are increasingly used to interpret mental health narratives in settings such as support chatbots, screening, and content moderation. A basic reliability question remains underexplored: when the same narrative is presented in different languages, does a model apply comparable evaluative standards? We study cross-linguistic variation in mental health evaluation between English and Chinese using two widely deployed LLMs (GPT-4o and Qwen3-32B). We separate (i) construct-level evaluative orientation from (ii) decision-level behavior on downstream tasks. At the construct level, we adapt validated psychometric stigma instruments spanning social, self, and health-professional stigma and quantify language-conditioned shifts in model responses. At the decision level, we evaluate models on binary stigma detection and four-way depression severity classification using semantically aligned English--Chinese datasets. Across instruments and models, Chinese prompts elicit systematically higher stigma scores than English prompts. These differences propagate to downstream decisions: Chinese prompts reduce sensitivity in stigma detection and induce a downward shift in predicted depression severity, producing substantially more under-estimation errors. Our findings show that prompt language can shift both evaluative orientation and effective decision thresholds, underscoring the need for multilingual evaluation and calibration before deploying LLMs in socially sensitive domains.

Language Shapes Mental Health Evaluations in Large Language Models

The evaluation of societal biases in NLP models is critically hindered by a geo-cultural gap. This leaves regions such as Latin America severely underserved, making it impossible to adequately assess or mitigate the perpetuation of harmful regional stereotypes in language technologies.

This paper presents LACES, a stereotype association dataset, for 15 Latin American countries. This dataset includes 4,789 stereotype associations\footnote{The de-identified dataset can be accessed via \href{https://github.com/fvialibre/LACES}{GitHub}}, manually created and annotated by 83 participants. The dataset was developed through targeted community partnerships across Latin America.

Additionally, in this paper, we propose a novel adaptive data collection methodology that uniquely integrates the sourcing of new stereotype entries and the validation of existing data within a single, unified workflow. This approach results in a resource with more unique stereotypes than previous static collection methods, enabling a more efficient stereotype collection. The paper further supports the quality of LACES by demonstrating reduced efficacy of debiasing methods on this dataset in comparison to existing popular stereotype benchmarks.

Adaptive Data Collection for Latin-American Community-sourced Evaluation of Stereotypes (LACES)

Stereotype repositories are critical to assess generative AI model safety, but currently lack adequate global coverage. It is imperative to prioritize targeted expansion, strategically addressing existing deficits, over merely increasing data volume. This work introduces a multilingual stereotype resource covering four sub-Saharan African countries that are severely underrepresented in NLP resources: Ghana, Kenya, Nigeria, and South Africa. By utilizing socioculturally-situated, community-engaged methods, including telephonic surveys moderated in native languages, we establish a reproducible methodology that is sensitive to the region's complex linguistic diversity and traditional orality. By deliberately balancing the sample across diverse ethnic and demographic backgrounds, we ensure broad coverage, resulting in a dataset of 3,534 stereotypes in English and 3,206 stereotypes across 15 native languages.

SAFARI: A Community-Engaged Approach and Dataset of Stereotype Resources in the Sub-Saharan African Context

Large language models (LLMs) are increasingly used as evaluative tools across languages, yet bias research remains overwhelmingly Anglocentric, with most studies conducted in English using Latin-script names. It remains unclear whether bias patterns generalize across linguistic contexts. We investigate this question and introduce the stereotype perceptual map, a framework for analyzing how ethnic groups are positioned along evaluative dimensions. 
Using 900,000 model responses over 45,000 names spanning 9 ethnicities, we evaluate model behavior across prompt languages (English, Chinese, Thai), writing scripts (Latin, Chinese, Thai), evaluative domains (competence, warmth), and models (GPT, DeepSeek). We find that ethnic bias hierarchies are jointly shaped by local linguistic context and model origin and differ substantially between Western-centric and Sinocentric models. DeepSeek exhibits highly stable rankings across conditions in math competence judgments, consistently placing Chinese at the top, followed by Russian, and White, Hispanic, and Black names at the bottom. GPT, by contrast, shows strong script-dependent reordering: Latin-script conditions form one stable cluster, while native-script conditions form another, with substantially lower cross-cluster correlations. We term this script-gated bias: transliterating the same names into a non-Latin script can activate a different evaluative frame and produce rankings that are sometimes inversely correlated with Latin-script results. Warmth evaluations are less stable than competence across both models.
Our findings demonstrate that multilingual bias cannot be characterized through single-language, single-script audits. For multilingual users, code-switching between languages can toggle between different bias regimes. Fairness evaluations for multilingual LLMs must therefore account for deployment language, writing system, and model origin to capture the full range of potentially harmful stereotypes these systems exhibit.

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