United States

Knowledge Graph-to-Text (G2T) generation involves verbalizing structured knowledge graphs into natural language text. Recent advancements in Pretrained Language Models (PLMs) have improved G2T performance, but their effectiveness relies on datasets with precise graph-text alignment. However, the scarcity of high-quality, general-domain G2T generation datasets restricts progress in the general-domain G2T generation research. To address this issue, we introduce Wikipedia Ontology-Free Graph-text dataset (WikiOFGraph), a new large-scale G2T dataset generated using a novel method that leverages Large Language Models (LLMs) and Data-QuestEval. Our dataset, which contains 5.85M general-domain graph-text pairs, offers high graph-text consistency without reliance on external ontologies. Experimental results demonstrate that PLM fine-tuned on WikiOFGraph outperforms those trained on other datasets across various evaluation metrics. Our method proves to be a scalable and effective solution for generating high-quality G2T data, significantly advancing the field of G2T generation.

ACL 2026

Ontology-Free General-Domain Knowledge Graph-to-Text Generation Dataset Synthesis using Large Language Model

## 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. 
* Familiar from NAACL but new to EMNLP, we’ve added an industry track.
* During the conference, “portals” will link virtual poster sessions to in-person conference participants during poster sessions each day.
* The first ACL-family conference in the United Arab Emirates.

 *Message from Noah A. Smith, University of Washington and Allen Institute for AI, Seattle, Washington, USA* 
***EMNLP 2022 General Chair***
 
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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.

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. 
[Full Message from General Chair Philipp Koehn](https://drive.google.com/file/d/1QiJFOmMjvDgsDo3i1XzlQnImxe3RU_Cy/view?usp=sharing) 
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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.

We present the first neural text-to-speech (TTS) implementation for Myaamia (Miami-Illinois), an Indigenous Algonquian language of North America. Developed in collaboration with the Myaamia Center at Miami University, our approach upholds principles of data sovereignty. Using 14,358 utterances (10.4 hours total, 8.18 hours for training) from seven speakers, we train and evaluate FastSpeech, Glow-TTS, and VITS, assessing synthesis quality through objective (MCD, F0 RMSE, duration RMSE) and subjective (expert evaluation) metrics. VITS outperforms other models in spectral and prosodic accuracy, but challenges remain in phonetic precision and prosody modeling. Our results confirm the feasibility of neural TTS for Myaamia, with direct implications for language learning and revitalization. This work offers a replicable framework for other low-resource Indigenous languages while ensuring ethical, linguistic data governance.

Neural Text-to-Speech for Myaamia: Speech Synthesis for an Indigenous Algonquian Language

We evaluate seven large language models—four proprietary and three open-weight—on bidirectional Lakota–English translation using 200 sentence pairs from the New Lakota Dictionary. Each model is evaluated with and without extended reasoning, where the provider's API permits. The best model (Gemini 3.1 Pro) achieves a mean chrF++ of 59.4 on Lakota→English and 42.6 on English→Lakota; the strongest open-weight model trails the proprietary leaders, and no model produces reliable translation in either direction. Two independent LLM judges from different model families agree substantially (Cohen's κ=0.75) that semantic equivalence ranges from 6% (GPT-5.2) to 60% (Gemini), diverging substantially from chrF++ scores. For the open-weight models, enabling reasoning changes refusal behavior far more than translation quality: it surfaces the limitation rather than overcoming it. Diacritic-normalization analysis shows models produce roughly correct base characters but place diacritical marks inconsistently. All results and evaluation code are publicly available at https://github.com/robotson/lakota-translation-benchmark.

Evaluating Frontier LLM Translation Capability for Lakota

Digital tools serving language revitalization tend to fall into two categories: 1) linguist-oriented documentation tools that prioritize annotation, morphological analysis, and archival preservation, and 2) community-facing applications that emphasize accessibility and language learning. Few systems integrate the former with the latter, and practical barriers — including the cost of computational expertise, single-user workflows, and limited data governance — further constrain their utility. These disconnects incur additional development and communication costs for revitalization teams consisting of linguists and community members. We introduce "langlit", a collaborative web-based platform that attempts to tailor documentation workflows for the language revitalization context within a single system. The platform integrates a finite-state morphological analyzer with a three-tier human-in-the-loop annotation workflow, searchable corpus interfaces with multiple query modalities, interactive word construction guided by the morphological grammar, corpus-linked hypothesis tracking with provenance, and a grammar-derived editable dictionary. All components share a single underlying FST grammar, and the system supports configurable access controls, collaborative editing, and optional LLM integration with transparent data handling. Designed for redeployment across languages through a modular architecture, "langlit" is published as an open-source repository on GitHub. We situate our system within the existing landscape of revitalization tools through a comparative analysis and discuss how integrated, community-informed design can better serve the specific goals of language revitalization.

Bridging Digital Tools for Linguistic Documentation and Revitalization

We present the first systematic benchmark of parameter-efficient fine-tuning (PEFT) for low-resource neural machine translation (NMT) of indigenous languages of the Americas. We evaluate eight PEFT methods alongside full fine-tuning on NLLB-200-distilled-600M across 13 indigenous-to-Spanish language pairs spanning four resource tiers (357-125,008 training sentences). OFT (Orthogonal Finetuning) achieves the highest development-set chrF++ among PEFT methods (26.63) while training only 0.28% of parameters. LoRA (Low-Rank Adaptation) offers a strong efficiency-quality tradeoff (25.27 chrF++, 0.19%). On held-out test data, full fine-tuning ranks first (25.12) with OFT a close second (25.06; p = 0.43). VeRA (Vector-based Random Matrix Adaptation) and Prefix Tuning consistently underperform. These results demonstrate that PEFT is a viable alternative to full fine-tuning for indigenous-language NMT.

A Systematic Comparison of Parameter-Efficient Fine-Tuning Techniques for Low-Resource Neural Machine Translation: Evidence from Indigenous Languages of the Americas

This paper presents a multi-dialect text classifier for Quechua that augments neural models with rule-based linguistic information to address challenges in low-resource, morphologically complex settings. The approach is built on a carefully curated dataset spanning multiple genres, including annotated parallel bible corpora, and encodes manually annotated lexical variation and polypersonal verbal agreement as explicit features within a transformer-based classifier. Results show that neural models substantially outperform statistical baselines, enabling highly accurate multi-class classification across 27 Quechua dialects. The impact of linguistic augmentation is context-dependent: gains are minimal in high-resource settings but more pronounced in low-resource and cross-domain conditions. Overall, this work aims to contribute to the development of dialect-sensitive NLP methods for Quechua and other low-resource, morphologically rich languages.

Linguistic Feature Tagging for Automatic Classification of 27 Closely-Related Quechua Varieties

Interlinear glossing is essential for the study and revitalization of endangered languages. However, it remains a time-consuming process that requires extensive linguistic expertise. Recent advances in Large Language Models (LLMs) offer a potential solution. In this research, we study the case of Mapudungun, an endangered language spoken in Chile and Argentina, to generate automatic interlinear glosses using the Gemini 2.5 Pro model. Our study investigates which information configuration through Retrieval-Augmented Generation (RAG) yields the best results. We compare the integration of a formal grammar, a dictionary, a small annotated corpus, and a combination of all these resources. Our evaluation shows that while dictionary integration causes a significant degradation in performance, grounding the model with a structured corpus maximizes accuracy relative to the resources employed. Notably, we find that a remarkably small dataset of 589 meaning units provides enough normative guidance to significantly improve the morphological tagging task. This work highlights the viability of utilizing minimally annotated corpora to assist in the documentation of morphologically complex languages.

What Resources Matter for Interlinear Glossing? Using LLMs and RAG for the Low-Resource Mapudungun Language

We introduce a finance & accounting benchmark (Finch) for evaluating AI agents on real-world, enterprise-grade professional workflows -- interleaving data entry, structuring, formatting, web search, cross-file retrieval, calculation, modeling, validation, translation, visualization, and reporting. Finch is sourced from authentic enterprise workspaces at Enron (15,000 spreadsheets and 500,000 emails from 150 employees) and other financial institutions, preserving in-the-wild messiness across multimodal artifacts (text, tables, formulas, charts, code, and images) and spanning diverse domains such as budgeting, trading, and asset management. We propose a workflow construction process that combines LLM-assisted discovery with expert annotation: (1) LLM-assisted, expert-verified derivation of workflows from real-world email threads and version histories of spreadsheet files, and (2) meticulous expert annotation for workflows, requiring over 700 hours of domain-expert effort. This yields 172 composite workflows with 384 tasks, involving 1,710 spreadsheets with 27 million cells, along with PDFs and other artifacts, capturing the intrinsically messy, long-horizon, knowledge-intensive, and collaborative nature of real-world enterprise work. We conduct both human and automated evaluations of frontier AI systems including GPT 5.1, Claude Sonnet 4.5, Gemini 3 Pro, Grok 4, and Qwen 3 Max, and GPT 5.1 Pro spends 16.8 minutes per workflow yet passes only 38.4% of workflows, while Claude Sonnet 4.5 passes just 25.0%. Comprehensive case studies further surface the challenges that real-world enterprise workflows pose for AI agents.

Finch: Benchmarking Finance & Accounting across Spreadsheet-Centric Enterprise Workflows

In modern industry systems like multi-turn chat agents, Text-to-SQL technology bridges natural language (NL) questions and database (DB) querying. The conversion of tabular DB results into NL representations (NLRs) enables the chat-based interaction. Currently, NLR generation is typically handled by large language models (LLMs), but information loss or errors in presenting tabular results in NL remains largely unexplored. This paper introduces a novel evaluation method - Combo-Eval - for judgment of LLM-generated NLRs that combines the benefits of multiple existing methods, optimizing evaluation fidelity and achieving a significant reduction in LLM calls by 25-61%. Accompanying our method is NLR-BIRD, the first dedicated dataset for NLR benchmarking. Through human evaluations, we demonstrate the superior alignment of Combo-Eval with human judgments, applicable across scenarios with and without ground truth references.

Can LLMs Narrate Tabular Data? An Evaluation Framework for Natural Language Representations of Text-to-SQL System Outputs

Human reasoning is inherently multimodal: when problems become difficult, we rarely think in words alone. We often externalize our reasoning by sketching diagrams or drawing grids to better understand the underlying conceptual structure and avoid mistakes. Building upon this, our research investigates a) whether grounding multi-hop textual–spatial stories into other modalities that capture the geometry of the scene, like visualization or grids, improves reasoning abilities compared to natural language text-based inference; and b) whether the model is able to decide effectively when it should rely on natural language text-based reasoning and when it should switch between modalities. To assess this, we introduce a switching metric based on trustworthiness and complexity signals, which estimates when grounding a spatial story into structure is likely to improve performance, taking a first step towards principled modality selection in large language models' reasoning.

Spatial Reasoning via Modality Switching Between Language and Symbolic Visualization

Mental health is an incredibly pressing issue in terms of societal, economic, and personal impact.  Although it's natural to conceive of it as a clinical domain, it's also very much a social science domain; moreover, language is one of the most important carriers of signal about individuals' lived experiences and mental state. In this talk I'll discuss the evolving landscape and needs of computational mental health research, what folks interested in NLP+CSS can contribute, and why mental health should be attractive to NLP+CSS researchers as a space to work in. 

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Neural Text-to-Speech for Myaamia: Speech Synthesis for an Indigenous Algonquian Language

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Next from ACL 2026

Neural Text-to-Speech for Myaamia: Speech Synthesis for an Indigenous Algonquian Language

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Self-supervised Graph Masking Pre-training for Graph-to-Text Generation

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