United States

Text normalization, the task of converting noisy, informal text into a standardized form - is a fundamental preprocessing step for many NLP applications. Despite the growing need for Kyrgyz language processing tools, to the best of our knowledge, no prior work has addressed automatic text normalization for Kyrgyz, a morphologically rich, low-resource Turkic language. In this paper, we present the first systematic study of Kyrgyz text normalization. We collect a dataset of 1.67 million noisy–clean text pairs sourced from YouTube comments, Instagram posts, and Telegram channels, where users frequently write without punctuation, capitalization, or standard spelling. Pairs were annotated with Gemini 3 Pro; the 1,000-example test set was fully verified by two native Kyrgyz speakers with adjudication, and a random subset of the training data was spot-checked, while the full 1.67M training set was not verified exhaustively. For continual pre-training, we additionally use a 538 MB Kyrgyz corpus compiled from news portals and books. We evaluate five systems: a rule-based baseline, zero-shot mT5, a fine-tuned mT5-small model, a continually pre-trained mT5-small followed by fine-tuning, and zero-shot Gemma 4. Our experiments show that fine-tuned mT5-small achieves a CER of 0.0796, outperforming the rule-based baseline (CER 0.2029), zero-shot mT5 (CER 0.9887), and zero-shot Gemma 4 (CER 0.1620), a roughly 32× larger model in a fine-tuned vs. zero-shot setting. Human evaluation by two native Kyrgyz speakers confirms these results, with fine-tuned mT5-small rated as correct in 99.8% of cases. We further analyze why continual pre-training with span corruption does not improve over direct fine-tuning, finding hallucination in 35/40 of the inspected failure cases (87.5%, 95% Wilson CI [74%, 95%]).

ACL 2026

Kyrgyz Text Normalization: A Comparative Study of Neural and Rule-Based Approaches

kyrgyz language

turkic languages

morphologically rich languages

text normalization

low-resource nlp

fine-tuning

#### *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.
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EACL 2026

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. 

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.

It is exciting to see the many developments in the field reflected at our conference, such as the advancement of large language models (born out of our community) into many application directions. This is not without challenges, partly due to unsolved problems, and partly due to misuse of the technology. All this is reflected well at ACL 2026. The conference also reflects that the field is not only about advancing technology, but also the science of fundamentally understanding language and how it is use by many different speaker communities. <br>
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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.

What have language models (LMs) learned about grammar? This question remains hotly debated, with major ramifications for linguistic theory. However, since probability and grammaticality are distinct notions in linguistics, it is not obvious what string probabilities can reveal about an LM's underlying grammatical knowledge. We present a theoretical analysis of the relationship between grammar, meaning, and string probability, based on simple assumptions about the generative process of corpus data. Our framework makes three predictions, which we validate empirically using 280K sentence pairs in English and Chinese: (1) correlation between the probability of strings within minimal pairs, i.e., string pairs with minimal semantic differences; (2) correlation between models' and humans' deltas within minimal pairs; and (3) poor separation in probability space between unpaired grammatical and ungrammatical strings. Our analyses give theoretical grounding for using probability to learn about LMs' structural knowledge, and suggest directions for future work in LM grammatical evaluation.

What Can String Probability Tell Us About Grammaticality?

We address the fundamental task of inferring cross-document coreference and hierarchy in scientific texts, which has important applications in knowledge graph construction, search, recommendation, and discovery. LLMs can struggle when faced with many long-tail technical concepts with nuanced variations. We present a novel method which generates context-dependent definitions of concept mentions by retrieving full-text literature, and uses the definitions to enhance detection of cross-document relations. We further generate relational definitions, which describe how two concept mentions are related or different, and design an efficient reranking approach to address the combinatorial explosion involved in inferring links across papers. In both fine-tuning and in-context learning settings we achieve large gains in performance. We provide analysis of generated definitions, shedding light on the relational reasoning ability of Large Language Models (LLMs) over fine-grained scientific concepts.

Inferring Scientific Cross-Document Coreference and Hierarchy with Definition-Augmented Relational Reasoning

When a news article describes immigration as an "economic burden" or a "humanitarian crisis," it selectively emphasizes certain aspects of the issue. Although this framing shapes how the public interprets such issues, audiences do not absorb frames passively but actively reorganize the presented information. While this relationship between source content and audience response is well-documented in the social sciences, NLP approaches often ignore it, detecting frames in articles and responses in isolation. We present the first computational framework for large-scale analysis of framing across source content (news articles) and audience responses (reader comments). Methodologically, we refine frame labels and develop a framework that reconstructs dominant frames in articles and comments from sentence-level predictions, and aligns articles with topically relevant comments. Applying our framework across eleven topics and two news outlets, we find that frame reuse in comments correlates highly across outlets, and that readers often selectively engage with frames of the articles. We release a frame classifier that performs well on both articles and comments, a dataset of article and comment sentences manually labeled for frames, and a large-scale dataset of articles and comments with predicted frame labels.

Retain or Reframe? A Computational Framework for the Analysis of Framing in News Articles and Reader Comments

Financial misinformation poses significant threats to financial market stability and individuals’ investment decisions. The multilingual environment and the inherent complexity of financial information present substantial challenges for Multilingual Financial Misinformation Detection (MFMD). Existing LLM-based approaches for financial misinformation detection primarily focus on English and a single financial misinformation detection task, which limits their ability to capture multilingual contexts and complex features. In this paper, we propose MFMDQwen, the first open-source LLM designed for MFMD tasks. Furthermore, we introduce MFMD4Instruction, the first instruction dataset supporting MFMD with LLMs, covering English, Chinese, Greek, and Bengali. We also construct MFMDBench, a benchmark dataset for evaluating the MFMD capabilities of LLMs. Experimental results on MFMDBench demonstrate that our model outperforms existing open-source LLMs.

MFMDQwen: Multilingual Financial Misinformation Detection Based on Large Language Model

Evaluation of multilingual large language models has grown rapidly in recent years, yet Marathi, spoken by over 83 million people across India, has received almost no systematic probing beyond surface-level benchmark tests. Most existing multilingual evaluations either omit Marathi entirely or rely on machine-translated test sets that fail to capture the morphological complexity that defines the language. We evaluate four models, namely Llama-3.1-8B, Llama-3.3-70B, Mistral-7B, and Qwen3-32B, on our manually curated Marathi dataset across three probing dimensions: Devanagari versus Romanized script, Marathi-English code-mixing, and syntactic structures including SOV word order, vibhakti case markers, verb gender agreement, and postpositions. Models are tested under English and Marathi instruction conditions across translation, similarity, grammaticality, and case marker tasks. Translation quality is evaluated using both token-level F1 and BERTScore to capture paraphrase equivalence beyond surface word overlap. All models drop between 7.9% and 20.5% on Romanized input. The negative subjunctive marker nasta is ignored by every model. Vibhakti case markers are consistently replaced with Hindi equivalents, revealing that multilingual training has not produced separate internal representations for Hindi and Marathi despite their distinct morphological systems. These findings reveal structural gaps in how current multilingual LLMs handle morphologically rich, low-resource Indic languages and point to specific areas where dedicated Marathi pretraining data would most benefit future work.

Beyond Accuracy: A Structured Error Analysis of Multilingual LLMs on Marathi Script Variation and Syntax

Improving the fairness of a language model is a goal that applies at every level of the model. In this paper, we evaluate a method targeting a foundational level: tokenization.
We present a multilingual evaluation of parity-aware tokenization under worst-$N$ optimization, extending PA-BPE to jointly optimize over the $N$ worst-compressed languages.
We evaluate this formulation for $N > 1$ across vocabulary sizes of 16K and 32K on the languages from the flores+ benchmark, using metrics that capture both efficiency and structural alignment.
Our results reveal that the effects of increasing $N$ are inconsistent across metrics and do not lead to major gains. Efficiency-oriented and boundary-level metrics show a modest tendency to improve at higher values of $N$, while structural alignment metrics (such as AST alignment and boundary crossing) exhibit no clear pattern, suggesting that compression fairness and linguistic structure are mainly orthogonal objectives. Script-level analysis further reveals uneven effects across writing systems, with several non-Latin scripts showing greater sensitivity to increasing $N$.

Evaluating Multilingual Tokenization under Worst-N Parity-Aware BPE

Multilingual LLMs are increasingly used as context-aware judges in real-world information systems under the assumption that equivalent content receives equivalent judgments across languages. We examine this assumption through brand safety, a global application where automated ratings can affect advertisers' reputations, publishers' revenues, and users' access to news. We construct a benchmark of LLM-generated safety ratings for 10,467 semantically aligned news articles across 13 languages. We find systematic cross-lingual disagreement appearing in more than 96\% of cases where at least one language receives a non-zero risk rating. Suitability ratings differ significantly by language, controlling for run, category, and article. In the main model, English, German, and French content is generally rated more strictly, while Polish, Hungarian, Greek, Turkish, and Persian content is rated more leniently. Robustness checks with two additional LLMs show that significant language effects persist, though directional patterns vary by model. These findings show that multilingual LLM safety judgments can produce unequal outcomes for semantically equivalent content.

Multilingual Disparities in LLM-Based Safety Judgments: Evidence from Brand Safety Applications

A common assumption holds that switching to a non-English language makes a multilingual RAG system easier to attack for personal information. On an English-source synthetic-PII corpus with five query languages and a two-stage defence (LLM input judge + regex output filter), the output-stage point estimates do not support that assumption: English has the highest observed unstructured-PII leak rate, and only English-vs-Swahili separates cleanly under our document-level bootstrap intervals. Once the input judge is added, residual leaks remain on Arabic and Swahili in this Qwen-mediated pipeline, and back-translating the query does not close the gap. Translator, judge, and generator share one model family, so we treat this as pipeline-conditional rather than a causal language ranking. As an oracle diagnostic on a separate n=17 multilingual-prompted-judge residual corner, attaching the gold corpus document to the input judge blocks 15/17 residual cells — a follow-up direction, not a deployed mitigation, since all BLOCK/ALLOW rates are on adversarial queries only and we measure no benign-query FPR or utility. The anonymous supplement contains code, corpora, queries, and per-trial JSONLs.

Where Privacy Risk Lives in English-Source Multilingual RAG: A Stage-Decomposed Audit Across Five Query Languages

As LLM-generated content proliferates online, texts are increasingly subject to repeated processing and translation by models, making it critical to understand how such iterative reprocessing reshapes language. Prior work has shown that this degrades factual content and reduces diversity, but the fine-grained linguistic shifts underlying these effects remain unexplored. We track changes in epistemic markers, grammatical voice, degree adverbs, and nominalisation density across 12 iterations of round-trip translation applied to 600 BBC News articles, varying intermediate language, translation model, and chain topology across 17 experimental configurations. We find a consistent epistemic shift: evidential and factive markers increase while hedges decline, potentially causing tentative claims to read as more certain. Concurrently, texts undergo register-level formalisation---informal degree adverbs give way to formal alternatives, active-voice density drops, by-phrase passives attrite disproportionately, and nominalisation density rises. We also record clear model-specific patterns for certain settings. These shifts erode the markers of source, register, and agency, offering a fine-grained account of the factual degradation reported in previous studies.

The Broken Telephone Changes Tone: Examining Nuanced Linguistic Cues in LLM Chains-of-Translation

Beginning in the mid-1980s, a “statistical revolution” in our field redefined what it meant to work on language from a computational perspective. Steve Abney, in his essay opening The Balancing Act: Combining Symbolic and Statistical Approaches to Language (Klavans and Resnik, eds., MIT Press, 1996), wrote: “In the space of the last ten years, statistical methods have gone from being virtually unknown in computational linguistics to being a fundamental given”. We are again caught up in revolutionary times. Except this time around, the revolution is hurtling forward even faster, and it is redefining not only the research we do, but what it means to do research in the first place, the role of research in society, and society itself. What does this mean for ACL – what defines us as a community, what is our role, and where do we go from here?

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What Can String Probability Tell Us About Grammaticality?

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What Can String Probability Tell Us About Grammaticality?

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

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