Austria

The proliferation of multilingual misinformation demands robust systems for cross-lingual fact-checked claim retrieval. This work addresses SemEval-2025 Shared Task 7, which challenges participants to retrieve fact-checks for social media posts across 14 languages, even when posts and fact-checks are in different languages. We propose a hybrid retrieval pipeline that integrates both sparse lexical matching techniques and dense semantic retrieval methods. Our approach implements the dynamic fusion of these complementary retrieval strategies and employs curriculum-trained rerankers to optimize retrieval performance. Our system achieves 67.2% cross-lingual and 86.01% monolingual accuracy on the Shared Task MultiClaim dataset.

ACL 2025

FactDebug at SemEval-2025 Task 7: Hybrid Retrieval Pipeline for Identifying Previously Fact-Checked Claims Across Multiple Languages

### 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)

workshop paper

Pre-trained language models have significantly advanced natural language processing (NLP), particularly in analyzing languages with complex morphological structures. This study addresses lemmatization for the Russian language, the errors in which can critically affect the performance of information retrieval, question answering, and other tasks. We present the results of experiments on generative lemmatization using pre-trained language models. Our findings demonstrate that combining generative models with the existing solutions allows achieving performance that surpasses current results for the lemmatization of Russian. This paper also introduces Rubic2, a new ensemble approach that combines the generative BART-base model, fine-tuned on a manually annotated data set of 2.1 million tokens, with the neural model called Rubic which is currently used for morphological annotation and lemmatization in the Russian National Corpus. Extensive experiments show that Rubic2 outperforms current solutions for the lemmatization of Russian, offering superior results across various text domains and contributing to advancements in NLP applications.

Rubic2: Ensemble Model for Russian Lemmatization

Large language models (LLMs) often reflect social biases present in their training data, including imbalances in how different genders are represented. While most prior work has focused on English, gender representation bias remains underexplored in morphologically rich languages where grammatical gender is pervasive. We present a method for detecting and quantifying such bias in Czech and Slovenian, using LLMs to classify gendered person references in LLM-generated narratives. Applying this method to outputs from a range of models, we find substantial variation in gender balance. While some models produce near-equal proportions of male and female references, others exhibit strong male overrepresentation. Our findings highlight the need for fine-grained bias evaluation in under-represented languages and demonstrate the potential of LLM-based annotation in this space. We make our code and data publicly available.

Gender Representation Bias Analysis in LLM-Generated Czech and Slovenian Texts

Proactive dialogue systems aim to empower chatbots with the capability of leading conversations towards specific targets, thereby enhancing user engagement and service autonomy. Existing systems typically target pre-defined keywords or entities, neglecting user attributes and preferences implicit in dialogue history, hindering the development of long-term user intimacy. To address these challenges, we take a radical step towards building a more human-like conversational agent by integrating proactive dialogue systems with long-term memory into a unified framework. Specifically, we define a novel task named Memory-aware Proactive Dialogue (MapDia). By decomposing the task, we then propose an automatic data construction method and create the first Chinese Memory-aware Proactive Dataset (ChMapData). Furthermore, we introduce a joint framework based on Retrieval Augmented Generation (RAG), featuring three modules: Topic Summarization, Topic Retrieval, and Proactive Topic-shifting Detection and Generation, designed to steer dialogues towards relevant historical topics at the right time. The effectiveness of our dataset and models is validated through both automatic and human evaluations.

Interpersonal Memory Matters: A New Task for Proactive Dialogue Utilizing Conversational History

Recent work has demonstrated that neural language models encode syntactic structures in their internal *representations*, yet the *derivations* by which these structures are built across layers remain poorly understood. 
In this paper, we introduce *Derivational Probing* to investigate how micro-syntactic structures (e.g., subject phrases) and macro-syntactic structures (e.g., the relationship between the root verb and its direct dependents) are progressively constructed as word embeddings propagate upward across layers.
Our experiments on BERT reveal a clear bottom-up derivation: micro-syntactic dependencies emerge in lower layers and are gradually integrated into a coherent macro-syntactic structure in higher layers.
Furthermore, an analysis on a subject-verb agreement task shows that the timing of macro-structure formation is critical for performance, suggesting an optimal intermediate range for integrating global syntactic information.

Derivational Probing: Unveiling the Layer-wise Construction of Syntactic Structures in Neural Language Models

This poster presents our submission to SemEval-2025 Task 1, which addresses the novel challenge of idiomatic image retrieval, ranking images based on their alignment with the idiomatic or literal meaning of a phrase in context. Idioms often defy literal interpretation, making them difficult for NLP systems, especially in multimodal settings. We propose a modular Mixture of Experts framework that separates the processing of idiomatic and literal expressions while leveraging both textual and visual cues. The system uses a BERT-based classifier to detect idiomaticity and activates specialized expert modules that integrate CLIP embeddings and contrastive learning for improved image relevance scoring. Trained on the AdMIRe and AStitchInLanguageModels datasets, our model achieves strong performance, with 65% Rank-1 accuracy on the test set and 50% on the extended test set. Through comprehensive evaluations and ablation studies, we demonstrate the efficacy of our architecture and its potential for advancing multimodal idiom understanding.

AIMA at SemEval-2025 Task 1: Bridging Text and Image for Idiomatic Knowledge Extraction via Mixture of Experts

This paper investigates the impact of data quality and processing strategies on emotion recognition in Brazilian Portuguese (PTBR) texts. We focus on data distribution, linguistic context, and augmentation techniques such as translation and synthetic data generation. To evaluate these aspects, we conduct experiments on the PTBR portion of the BRIGHTER dataset, a manually curated multilingual dataset containing nearly 100,000 samples, of which 4,552 are in PTBR. Our study encompasses both multi-label emotion detection (presence/absence classification) and emotion intensity prediction (0 to 3 scale), following the SemEval 2025 Track 11 setup. Results demonstrate that emotion intensity labels enhance model performance after discretization, and that smaller multilingual models can outperform larger ones in low-resource settings. Our official submission ranked 6th, but further refinements improved our ranking to 3rd, trailing the top submission by only 0.047, reinforcing the significance of a data-centric approach in emotion recognition.

AKCIT at SemEval-2025 Task 11: Investigating Data Quality in Portuguese Emotion Recognition

In SemEval-2025 Task 7: Multilingual and Cross-lingual Fact-Checked Claim Retrieval, we frame the problem as a Learning-to-Rank task and adopt a bi-encoder architecture based on a pre-trained transformer model fine-tuned for sentence similarity. For multilingual retrieval, training incorporates both the original source languages and their English translations, while cross-lingual retrieval relies solely on English translations. Our approach emphasizes efficiency by using lightweight models with fewer than 500M parameters and leveraging Kaggle T4 GPUs for training. Despite these constraints, our method achieved strong performance, ranking 10th in the multilingual track with a 92% Success@10 score and 5th in the cross-lingual track with an 80% Success@10.

fact check AI at SemEval-2025 Task 7: Multilingual and Crosslingual Fact-checked Claim Retrieval

The LLMs4Subjects shared task invited system contributions that leverage a technical library’s tagged document corpus to learn document subject tagging, i.e., proposing adequate subjects given a document’s title and abstract. To address the imbalance of this training corpus, team LA²I²F devised a semantic retrieval-based system fusing the results of ontological and analogical reasoning in embedding vector space. Our results outperformed a naive baseline of prompting a llama 3.1-based model, whilst being computationally more efficient and competitive with the state of the art.

LA²I²F at SemEval-2025 Task 5: Reasoning in Embedding Space - Fusing Analogical and Ontology-based Reasoning for Document Subject Tagging

This paper presents the Annif system in SemEval-2025 Task 5 (LLMs4Subjects), which focussed on subject indexing using large language models (LLMs). The task required creating subject predictions for bibliographic records from the bilingual TIBKAT database using the GND subject vocabulary. Our approach combines traditional natural language processing and machine learning techniques implemented in the Annif toolkit with innovative LLM-based methods for translation and synthetic data generation, and merging predictions from monolingual models. The system ranked first in the all-subjects category and second in the tib-core-subjects category in the quantitative evaluation, and fourth in qualitative evaluations. These findings demonstrate the potential of combining traditional XMTC algorithms with modern LLM techniques to improve the accuracy and efficiency of subject indexing in multilingual contexts.

Annif at SemEval-2025 Task 5: Traditional XMTC augmented by LLMs

In SemEval 2025 Task 9: The Food Hazard Detection Challenge, we explored text-based food hazard prediction with long tail distributed classes. The task was divided into two subtasks:
(1) predicting whether a web text implies one of ten food-hazard categories and identifying the associated food category, and
(2) providing a more fine-grained classification by assigning a specific label to both the hazard and the product.
Our findings highlight that large language model-generated synthetic data can be highly effective for oversampling long-tail distributions. Furthermore, we find that fine-tuned encoder-only, encoder-decoder, and decoder-only systems achieve comparable maximum performance across both subtasks. During this challenge, we gradually released (under CC BY-NC-SA 4.0) a novel set of 6,644 manually labeled food-incident reports.

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