Austria

Named Entity Recognition (NER) for low-resource variants of English remains challenging, as most NER models are trained on datasets predominantly focused on American or British English. While recent work has shown that proprietary Large Language Models (LLMs) can perform NER effectively in low-resource settings through in-context learning, practical deployment is limited by their high computational costs and privacy concerns. Open-source Small Language Models (SLMs) offer promising alternatives, but the tendency of these Language Models (LM) to hallucinate poses challenges for production use. To address this, we introduce SLENDER, a novel output format for LM-based NER that achieves a three-fold reduction in inference time on average compared to JSON format, which is widely used for structured outputs. Our approach using Gemma-2-9B-it with the SLENDER output format and constrained decoding in zero-shot settings outperforms the en_core_web_trf model from SpaCy, an industry-standard NER tool, in all five regions of the Worldwide test set.

ACL 2025

SLENDER: Structured Outputs for SLM-based NER in Low-Resource Englishes

output formats

low-resource englishes

structured outputs

small language models

prompting

named entity recognition

constrained decoding

#### *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.
For the first time EACL is being hosted in the African continent. This is an important milestone for our community, and we are grateful to our Moroccan hosts for enabling this historic moment by bringing this edition of EACL to Rabat. We are also delighted that the Second Arabic NLP School is co-located with EACL. We hope attendees enjoy this wonderful opportunity to strengthen ties with the Computational Linguistics communities across the African continent. *[Read full message](https://drive.google.com/file/d/14NlmHvwM6fPJuMmOvVh7K0vtQbEyv3SZ/view?usp=sharing)* 

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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. 

poster

### Welcome to The 63rd Annual Meeting of the Association for Computational Linguistics (ACL 2025)

Message from the General Chair: 
*It is my great pleasure and honor to welcome you to the 63rd Annual Meeting of the Association for Computational Linguistics (ACL 2025), held in beautiful Vienna, Austria, from July 27 to August 1, 2025. ACL2025continues our field’s 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.* 
[Read more](https://drive.google.com/file/d/1GI_hvOpjswAuYdUTromfeDiPpCcqidwg/view?usp=sharing)

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Welcome to The 63rd Annual Meeting of the Association for Computational Linguistics (ACL 2025)

Virtual Teaching Assistants (VTAs) powered by Large Language Models (LLMs) have the potential to enhance student learning by providing instant feedback and facilitating multi-turn interactions. However, empirical studies on their effectiveness and acceptance in real-world classrooms are limited, leaving their practical impact uncertain. In this study, we develop an LLM-based VTA and deploy it in an introductory AI programming course with 477 graduate students. To assess how student perceptions of the VTA’s performance evolve over time, we conduct three rounds of comprehensive surveys at different stages of the course. Additionally, we analyze 3,869 student–VTA interaction pairs to identify common question types and engagement patterns. We then compare these interactions with traditional student-human instructor interactions to evaluate the VTA’s role in the learning process. Through a large-scale empirical study and interaction analysis, we assess the feasibility of deploying VTAs in real-world classrooms and identify key challenges for broader adoption. Finally, we release the source code of our VTA system, fostering future advancements in AI-driven education.

A Large-Scale Real-World Evaluation of an LLM-Based Virtual Teaching Assistant

Effective content moderation is essential for video platforms to safeguard user experience and uphold community standards. While traditional video classification models effectively handle well-defined moderation tasks, they struggle with complicated scenarios such as implicit harmful content and contextual ambiguity. Multimodal large language models (MLLMs) offer a promising solution to these limitations with their superior cross-modal reasoning and contextual understanding. However, two key challenges hinder their industrial adoption. First, the high computational cost of MLLMs makes full-scale deployment impractical. Second, adapting generative models for discriminative classification remains an open research problem. In this paper, we first introduce an efficient method to transform a generative MLLM into a multimodal classifier using minimal discriminative training data. To enable industry-scale deployment, we then propose a router-ranking cascade system that integrates MLLMs with a lightweight router model. Offline experiments demonstrate that our MLLM-based approach improves F1 score by 66.50% over traditional classifiers while requiring only 2% of the fine-tuning data. Online evaluations show that our system increases automatic content moderation volume by 41%, while the cascading deployment reduces computational cost to only 1.5% of direct full-scale deployment.

Filter-And-Refine: A MLLM Based Cascade System for Industrial-Scale Video Content Moderation

Ambiguous user queries pose a significant challenge in task-oriented dialogue systems relying on information retrieval. While Large Language Models (LLMs) have shown promise in generating clarification questions to tackle query ambiguity, they rely solely on the top-k retrieved documents for clarification which fails when ambiguity is too high to retrieve relevant documents in the first place. Traditional approaches 
lack principled mechanisms to determine when to use broad domain knowledge vs specific retrieved document context for clarification. We propose AsK, a novel hybrid approach that dynamically chooses between document-based or aspect-based clarification based on query ambiguity. Our approach requires no labeled clarification data and introduces: (1) Weakly-supervised Longformer-based ambiguity analysis, (2) Automated domain-specific aspect generation using clustering and LLMs and (3) LLM-powered clarification generation. AsK demonstrates significant improvements over baselines in both single-turn and multi-turn settings (recall@5 gain of ~20%) when evaluated on product troubleshooting and product search datasets.

ASK: Aspects and Retrieval based Hybrid Clarification in Task Oriented Dialogue Systems

Electronic Health Records contain vast amounts of valuable clinical data, much of which is stored as unstructured text. Extracting meaningful clinical events (e.g., disorders, symptoms, findings, medications, and procedures etc.) in context within real-world healthcare settings is crucial for enabling downstream applications such as disease prediction, clinical coding for billing and decision support.
After Named Entity Recognition and Linking (NER+L) methodology, the identified concepts need to be further classified (i.e. contextualized) for distinct properties such as their relevance to the patient, their temporal and negated status for meaningful clinical use. 
We present a solution that, using an existing NER+L approach - MedCAT, classifies and contextualizes medical entities at scale. We evaluate the NLP approaches through 14 distinct real-world clinical text classification projects, testing our suite of models tailored to different clinical NLP needs. For tasks requiring high minority class recall, BERT proves the most effective when coupled with class imbalance mitigation techniques, outperforming Bi-LSTM with up to 28%. For majority class focused tasks, Bi-LSTM offers a lightweight alternative with, on average, 32% faster training time and lower computational cost. Importantly, these tools are integrated into an openly available library, enabling users to select the best model for their specific downstream applications.

A Framework for Flexible Extraction of Clinical Event Contextual Properties from Electronic Health Records

With the emergence of ChatGPT, Transformer models have significantly advanced text classification and related tasks. Decoder-only models such as Llama exhibit strong performance and flexibility, yet they suffer from inefficiency on inference due to token-by-token generation, and their effectiveness in text classification tasks heavily depends on prompt quality. Moreover, their substantial GPU resource requirements often limit widespread adoption. Thus, the question of whether smaller language models are capable of effectively handling text classification tasks emerges as a topic of significant interest. However, the selection of appropriate models and methodologies remains largely underexplored. In this paper, we conduct a comprehensive evaluation of prompt engineering and supervised fine-tuning methods for transformer-based text classification. Specifically, we focus on practical industrial scenarios, including email classification, legal document categorization, and the classification of extremely long academic texts. We examine the strengths and limitations of smaller models, with particular attention to both their performance and their efficiency in Video Random-Access Memory (VRAM) utilization, thereby providing valuable insights for the local deployment and application of compact models in industrial settings.

Small Language Models in the Real World: Insights from Industrial Text Classification

Text chunking is fundamental to modern retrieval-augmented systems, yet existing methods often struggle with maintaining semantic coherence, both within and across chunks, while dealing with document structure and noise. We present AutoChunker, a bottom-up approach for text chunking that combines document structure awareness with noise elimination. AutoChunker leverages language models to identify and segregate logical units of information (a chunk) while preserving document hierarchy through a tree-based representation. To evaluate the chunking operator, we introduce a comprehensive evaluation framework based on five core tenets: noise reduction, completeness, context coherence, task relevance, and retrieval performance. Experimental results on Support and Wikipedia articles demonstrate that AutoChunker significantly outperforms existing methods, reducing noise while improving chunk completeness compared to state-of-the-art baselines. When integrated with an online product support system, our approach led to improvements in retrieval performance and customer return rates. Our work not only advances the state of text chunking but also provides a standardized framework for evaluating chunking strategies, addressing a critical gap in the field.

AutoChunker: Structured Text Chunking and its Evaluation

Large Language Models (LLMs) enable natural language to SQL conversion, allowing users to query databases without SQL expertise. However, generating accurate, efficient queries is challenging due to ambiguous intent, domain knowledge requirements, and database constraints. Extensive reasoning improves SQL quality but increases computational costs and latency. We propose SQLGenie, a practical system for reliable SQL generation. It consists of three components: (1) \textbf{Table Onboarder}, which analyzes new tables, optimizes indexing, partitions data, identifies foreign key relationships, and stores schema details for SQL generation; (2) \textbf{SQL Generator}, an LLM-based system producing accurate SQL; and (3) \textbf{Feedback Augmentation}, which filters correct query-SQL pairs, leverages multiple LLM agents for complex SQL, and stores verified examples. SQLGenie achieves state-of-the-art performance on public benchmarks (92.8\% execution accuracy on WikiSQL, 82.1\% of Spider, 73.8\% on BIRD) and internal datasets, surpassing the best single-LLM baseline by 21.5\% and the strongest pipeline competitor by 5.3\%. Its hybrid variant optimally balances accuracy and efficiency, reducing generation time by 64\% compared to traditional multi-LLM approaches while maintaining competitive accuracy.

SQLGenie: A Practical LLM based System for Reliable and Efficient SQL Generation

Enterprise search systems often struggle to retrieve accurate, domain-specific information due to semantic mismatches and overlapping terminologies. These issues can degrade the performance of downstream applications such as knowledge management, customer support, and retrieval-augmented generation agents. To address this challenge, we propose a scalable hard-negative mining framework tailored specifically for domain-specific enterprise data. Our approach dynamically selects semantically challenging but contextually irrelevant documents to enhance deployed re-ranking models.

Our method integrates diverse embedding models, performs dimensionality reduction, and uniquely selects hard negatives, ensuring computational efficiency and semantic precision. Evaluation on our proprietary enterprise corpus (cloud services domain) demonstrates substantial improvements of 15\% in MRR@3 and 19\% in MRR@10 compared to state-of-the-art baselines and other negative sampling techniques. Further validation on public domain-specific datasets (FiQA, Climate Fever, TechQA) confirms our method's generalizability and readiness for real-world applications.

Hard Negative Mining for Domain-Specific Retrieval in Enterprise Systems

Determining company similarity is a vital task in finance, underpinning risk management, hedging, and portfolio diversification. Practitioners often rely on sector and industry classifications such as SIC and GICS codes to gauge similarity, the former being used by the U.S. Securities and Exchange Commission (SEC), and the latter widely used by the investment community. Since these classifications lack granularity and need regular updating, using clusters of embeddings of company descriptions has been proposed as a potential alternative, but the lack of interpretability in token embeddings poses a significant barrier to adoption in high-stakes contexts. Sparse Autoencoders (SAEs) have shown promise in enhancing the interpretability of Large Language Models (LLMs) by decomposing Large Language Model (LLM) activations into interpretable features. Moreover, SAEs capture an LLM's internal representation of a company description, as opposed to semantic similarity alone, as is the case with embeddings. We apply SAEs to company descriptions, and obtain meaningful clusters of equities. We benchmark SAE features against SIC-codes, Industry codes, and Embeddings. Our results demonstrate that SAE features surpass sector classifications and embeddings in capturing fundamental company characteristics. This is evidenced by their superior performance in correlating logged monthly returns – a proxy for similarity – and generating higher Sharpe ratios in co-integration trading strategies, which underscores deeper fundamental similarities among companies. Finally, we verify the interpretability of our clusters, and demonstrate that sparse features form simple and interpretable explanations for our clusters.

Interpretable Company Similarity with Sparse Autoencoders

We present the e-Llama models: 8 billion and 70 billion parameter large language models that are adapted towards the e-commerce domain.
These models are meant as foundation models with deep knowledge about e-commerce, that form a base for instruction- and fine-tuning.
The e-Llama models are obtained by continuously pretraining the Llama 3.1 base models on 1 trillion tokens of domain-specific data.

We discuss our approach and motivate our choice of hyperparameters with a series of ablation studies.
To quantify how well the models have been adapted to the e-commerce domain, we define and implement a set of multilingual, e-commerce specific evaluation tasks.

We show that, when carefully choosing the training setup, the Llama 3.1 models can be adapted towards the new domain without sacrificing significant performance on general domain tasks.
We also explore the possibility of merging the adapted model and the base model for a better control of the performance trade-off between domains.

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