Austria

Natural language understanding systems struggle with low-resource languages, including many dialects of high-resource ones. Dialect-to-standard normalization attempts to tackle this issue by transforming dialectal text so that it can be used by standard-language tools downstream. In this study, we tackle this task by introducing a new normalization method that combines rule-based linguistically informed transformations and large language models (LLMs) with targeted few-shot prompting, without requiring any parallel data. We implement our method for Greek dialects and apply it on a dataset of regional proverbs, evaluating the outputs using human annotators. We then use this dataset to conduct downstream experiments, finding that previous results regarding these proverbs relied solely on superficial linguistic information, including orthographic artifacts, while new observations can still be made through the remaining semantics.

ACL 2025

Dialect Normalization using Large Language Models and Morphological Rules

software and tools

dialects and language varieties

less-resourced languages

linguistic variation

cross-lingual transfer

### Welcome to the 17th Conference of the European Chapter of the Association for Computational Linguistics (EACL) 
Continuing its mission of expanding and involving the science community of all European countries, EACL had selected the Ukrainian community for the 16th EACL, which took place online due to the COVID pandemic. Unfortunately, the ongoing war made the organisation in Kyiv impossible. Considering the importance of physical interaction among researchers, especially after the restrictions imposed by the COVID pandemic, in addition to an online mode, the conference will be held in Dubrovnik, Croatia, from 2 to 6 of May, 2023. The original aim of strengthening the connection with the Ukrainian community will not change as our program will feature a dedicated session and a workshop to highlight work on Ukrainian language technologies. As the flagship European conference in the field of computational linguistics, EACL welcomes European and international researchers covering a broad spectrum of research areas that are concerned with computational approaches to natural language.

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EACL 2023

As the flagship European conference in the field of computational linguistics, EACL welcomes European and international researchers covering a broad spectrum of research areas that are concerned with computational approaches to natural language.

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

Pragmatic reasoning—the ability to infer meaning beyond literal interpretation—is crucial for social cognition and communication. While LLMs have been benchmarked for their pragmatic understanding, improving their performance remains underexplored. Existing methods rely on annotated labels but overlook the reasoning process humans naturally use to interpret implicit meaning. To bridge this gap, we introduce a novel pragmatic dataset that includes *explicit reasoning (thoughts)* for both correct and incorrect interpretations. Through preference-tuning and supervised fine-tuning, we demonstrate that thought-based learning significantly enhances LLMs' pragmatic understanding, improving accuracy by 11.12% across model families. We further discuss a transfer-learning study where we evaluate the performance of *thought*- based training for the tasks not seen during the train time, and observed an improvement of 16.10% compared to *label* trained models.

Understand the Implication: Learning to Think for Pragmatic Understanding

Pre-trained language models (PLMs) fragment numerals and units that express quantities in arbitrary ways, depending on their subword vocabulary. Consequently, they are unable to contextualize the fragment embeddings well enough to be proficient with dense retrieval in domains like e-commerce and finance. Arithmetic inequality constraints (``laptop under 2 lb'') offer additional challenges. In response, we propose DeepQuant, a dense retrieval system built around a dense multi-vector index, but carefully engineered to elicit and exploit quantities and associated comparison intents. A novel component of our relevance score compares two quantities with compatible units, conditioned on a proposed comparison operator. The uncertain extractions of numerals, units and comparators are marginalized in a suitable manner. On two public and one proprietary e-commerce benchmark, DeepQuant is both faster and more accurate than popular PLMs. It also beats several competitive sparse and dense retrieval systems that do not take special cognizance of quantities.

Dense Retrieval with Quantity Comparison Intent

Large language models (LLMs) achieve strong performance on plain text tasks but underperform on structured data like tables and databases. Potential challenges arise from their underexposure during pre-training and rigid text-to-structure transfer mechanisms. Unlike humans who seamlessly apply learned patterns across data modalities, LLMs struggle to infer implicit relationships embedded in tabular formats, especially in the absence of explicit structural guidance. To bridge this cognitive gap, we introduce Contrastive Retrieval-Augmented Generation on Experience (CoRE), a framework that builds experience memory representations and enhances generalization through contrastive In-Context Learning (ICL) to simulate human-like knowledge transfer. Experiments on Text-to-SQL and TableQA show CoRE significantly improves performance, achieving average gains of 3.44% and 4.24%, with up to 17.2% on challenging tasks. Our Monte Carlo Tree Search (MCTS)-generated Experience Memory expands training data 8-9×, enhancing diversity and domain coverage. This training-free and continual method propels LLMs toward structured knowledge expertise.

Toward Structured Knowledge Reasoning: Contrastive Retrieval-Augmented Generation on Experience

We investigate the linguistic abilities of multimodal large language models in reference resolution tasks featuring simple but abstract visual stimuli such as color patches and color grids. Although the task may not seem challenging for today's language models, being straightforward for human dyads, we consider it a highly relevant probe of the pragmatic capabilities of MLLMs. Indeed, our results and analyses suggest that basic pragmatic capabilities such as context-dependent interpretation of color descriptions still constitute major challenges for state-of-the-art MLLMs.

Are Multimodal Large Language Models Pragmatically Competent Listeners in Simple Reference Resolution Tasks?

Large language models (LLMs) are arguably the most predictive models of human cognition available. Despite their impressive human-alignment, LLMs are often labeled as "just next-token predictors" that purportedly fall short of genuine cognition. We argue that these deflationary claims need further justification. Drawing on prominent cognitive and artificial intelligence research, we critically evaluate two forms of "Justaism" that dismiss LLM cognition by labeling LLMs as "just" simplistic entities without specifying or substantiating the critical capacities they supposedly lack. Our analysis highlights the need for a more measured discussion of LLM cognition, aiming to better inform future research and the development of artificial intelligence.

A rebuttal of two common deflationary stances against LLM cognition

Information Extraction (IE) systems are traditionally domain-specific, requiring costly
adaptation that involves expert schema design,
data annotation, and model training. While
Large Language Models have shown promise
in zero-shot IE, performance degrades significantly in unseen domains where label definitions differ. This paper introduces GUIDEX,
a novel method that automatically defines
domain-specific schemas, infers guidelines,
and generates synthetically labeled instances,
allowing for better out-of-domain generalization. Fine-tuning Llama 3.1 with GUIDEX
sets a new state-of-the-art across seven zeroshot Named Entity Recognition benchmarks.
Models trained with GUIDEX gain up to 7 F1
points over previous methods without humanlabeled data, and nearly 2 F1 points higher
when combined with it. Models trained on
GUIDEX demonstrate enhanced comprehension of complex, domain-specific annotation
schemas. Code, models, and synthetic datasets
are available at neilus03.github.io/guidex.com

GUIDEX: Guided Synthetic Data Generation for Zero-Shot Information Extraction

Emotions have been shown to play a role in argument convincingness, yet this aspect is underexplored in the natural language processing (NLP) community. Unlike prior studies that use static analyses, focus on a single text domain or language, or treat emotion as just one of many factors, we introduce a dynamic framework inspired by manipulation checks commonly used in psychology and social science; leveraging LLM-based manipulation checks, this framework examines the extent to which perceived emotional intensity influences perceived convincingness. Through human evaluation of arguments across different languages, text domains, and topics, we find that in over half of cases, judgments of convincingness remain unchanged despite variations in perceived emotional intensity; when emotions do have an impact, they more often enhance rather than weaken convincingness. We further analyze how 11 LLMs behave in the same scenario, finding that while LLMs generally mirror human patterns, they struggle to capture nuanced emotional effects in individual judgments.

Do Emotions Really Affect Argument Convincingness? A Dynamic Approach with LLM-based Manipulation Checks

Process Reward Models (PRMs) have demonstrated promising results in mathematical reasoning, but existing process annotation approaches, whether through human annotations or Monte Carlo simulations, remain computationally expensive. In this paper, we introduce Step COmpression for Process Estimation (SCOPE), a novel compression-based approach that significantly reduces annotation costs. We first translate natural language reasoning steps into code and normalize them through Abstract Syntax Tree, then merge equivalent steps to construct a prefix tree. Unlike simulation-based methods that waste numerous samples on estimation, SCOPE leverages a compression-based prefix tree where each root-to-leaf path serves as a training sample, reducing the complexity from O(NMK) to O(N) We construct a large-scale dataset containing 509K samples with only 5% of the computational resources required by previous methods. Empirical results demonstrate that PRMs trained on our dataset consistently outperform existing automated annotation approaches on both Best-of-N strategy and ProcessBench.

SCOPE: Compress Mathematical Reasoning Steps for Efficient Automated Process Annotation

Citation context analysis (CCA) is a field of research studying the role and purpose of citation in scientific discourse. While most of the efforts in CCA have been focused on elaborate characterization schemata to assign function or intent labels to individual citations, the citation context as the basis for such a classification has received rather limited attention. This relative neglect, however, has led to the precedence of vague definitions and restricting assumptions, limiting the citation context in its expressiveness. It is a common practice, for example, to restrict the context to the citing sentence. While this might be enough to cover mentions and background citations, more influential ones are often thoroughly discussed, extending beyond a one-sentence context window. To address this concern, we analyze the semantic structure of citation contexts in terms of their elemental dimensions and distribution throughout the citing text. To evaluate this approach, we construct and publish the FineCite Corpus containing 1,056 manually annotated fine-grained citation contexts. Our experiments on established CCA benchmarks demonstrate the effectiveness of our finer-grained context definition, showing improvement compared to state-of-the-art approaches. We will release our code and dataset to the public upon acceptance.

FINECITE: A Novel Approach on Fine-Grained Citation Context Analysis

Real-world applications of KBQA require models to handle unanswerable questions with a limited volume of in-domain labeled training data. We propose the novel task of few-shot transfer for KBQA with unanswerable questions and contribute two new datasets for performance evaluation. We present FUn-FuSIC -- a novel solution for our task that extends FuSIC-KBQA, the state-of-the-art few-shot transfer model for answerable-only KBQA. We first note that FuSIC-KBQA's iterative repair makes a strong assumption that all questions are answerable. As a remedy, we propose Feedback for Unanswerability (FUn), which uses iterative repair using feedback from a suite of strong and weak verifiers, and an adaptation of self-consistency for unanswerabilty to better assess the answerability of a question. Our experiments show that FUn-FuSIC significantly outperforms suitable adaptations of multiple LLM-based and supervised SoTA models on our task, while establishing a new SoTA for answerable few-shot transfer as well.

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Understand the Implication: Learning to Think for Pragmatic Understanding

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Dialect Representation Learning with Neural Dialect-to-Standard Normalization

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