GenAI4ELab/papercli-papers huggingface dataset:
IEGPS-CSIC at SemEval-2025 Task 11: BERT-based approach for Multi-label Emotion Detection in English and Russian texts
Authors: Albina Sarymsakova; Patricia Martin - Rodilla
Venue: ACL 2025 (Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025))

Abstract: This paper presents an original approach for SemEval 2025 Task 11. Our study investigates various strategies to improve Text-Based Multi-label Emotion Detection task. Through experimental endeavors, we explore the benefits of contextualized vector representations by comparing multiple BERT models, including those specifically trained for emotion recognition. Additionally, we examine the impact of hyperparameters adjustments on model performance. For Subtask A, our approach achieved F1 scores of 0.71 on the English dataset and 0.84 on the Russian dataset. Our findings underscore that (1) monolingual BERT models demonstrate superior performance for English, whereas multilingual BERT models perform better for Russian; (2) pretrained emotion detection models proving less effective for this specific task compared to models with reduced vocabulary and embeddings focused on specific languages;(3) exclusive use of BERT-based models, without incorporating additional methods or optimization techniques, demonstrates promising results for multilabel emotion detection.
CHILL at SemEval-2025 Task 2: You Can’t Just Throw Entities and Hope—Make Your LLM to Get Them Right
Authors: Jaebok Lee; Yonghyun Ryu; Seongmin Park; Yoonjung Choi
Venue: ACL 2025 (Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025))

Abstract: In this paper, we describe our approach for the SemEval 2025 Task 2 on Entity-Aware Machine Translation (EA-MT).Our system aims to improve the accuracy of translating named entities by combining two key approaches: Retrieval Augmented Generation (RAG) and iterative self-refinement techniques using Large Language Models (LLMs).A distinctive feature of our system is its self-evaluation mechanism, where the LLM assesses its own translations based on two key criteria: the accuracy of entity translations and overall translation quality. We demonstrate how these methods work together and effectively improve entity handling while maintaining high-quality translations.
AlexUNLP-NB at SemEval-2025 Task 1: A Pipeline for Idiom Disambiguation and Visual Representation
Authors: Mohamed Badran; Youssof Nawar; Nagwa El - Makky
Venue: ACL 2025 (Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025))

Abstract: This paper describes our system developed for SemEval-2025 Task 1, subtask A. This sharedsubtask focuses on multilingual idiom recognition and the ranking of images based on howwell they represent the sense in which a nominal compound is used within a given contextual sentence. This study explores the use of a pipeline, where task-specific models are sequentially employed to address each problem step by step. The process involves three key steps: first, identifying whether idioms are in their literal or figurative form; second, transforming them if necessary; and finally, usingthe final form to rank the input images.
RACAI at SemEval-2025 Task 7: Efficient adaptation of Large Language Models for Multilingual and Crosslingual Fact-Checked Claim Retrieval
Authors: Radu - Gabriel Chivereanu; Dan Tufis
Venue: ACL 2025 (Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025))

Abstract: The paper details our approach to SemEval 2025 Shared Task 7: Multilingual and Crosslingual Fact-Checked Claim Retrieval.We investigate how large language models (LLMs) designed for general-purpose retrieval via text-embeddings can be adapted for fact-checked claim retrieval across multiple languages, including scenarios where the query and fact-check are in different languages. The experiments involve fine-tuning with a contrastive objective, resulting in notable gains in both accuracy and efficiency over the baseline retrieval model. We evaluate cost-effective techniques such as LoRA and QLoRA and Prompt Tuning.Additionally, we demonstrate the benefits of Matryoshka embeddings in minimizing the memory footprint of stored embeddings, reducing the system requirements for a fact-checking system.
FII the Best at SemEval 2025 Task 2: Steering State-of-the-art Machine Translation Models with Strategically Engineered Pipelines for Enhanced Entity Translation
Authors: Delia - Iustina Grigorita; Tudor - Constantin Pricop; Sergio - Alessandro Suteu; Daniela Gifu; Diana Trandabat
Venue: ACL 2025 (Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025))

Abstract: Entity-Aware Machine Translation (EAMT) aims to enhance the accuracy of machine translation (MT) systems in handling named entities, including proper names, domain-specific terms, and structured references. Conventional MT models often struggle to accurately translate these entities, leading to errors that affect comprehension and reliability. In this paper, we present a promising approach for SemEval 2025 Task 2, focusing on improving EAMT in ten target languages. The methodology is based on two complementary strategies: (1) multilingual Named Entity Recognition (NER) and structured knowledge bases for preprocessing and integrating entity translations, and (2) large language models (LLMs) enhanced with optimized prompts and validation mechanisms to improve entity preservation. By combining structured knowledge with neural approaches, this system aims to mitigate entity-related translation errors and enhance the overall performance of MT models. Among the systems that do not use gold information, retrieval-augmented generation (RAG), or fine-tuning, our approach ranked 1st with the second strategy and 3rd with the first strategy.
ZJUKLAB at SemEval-2025 Task 4: Unlearning via Model Merging.
Authors: Haoming Xu; Shuxun Wang; Yanqiu Zhao; Yi Zhong; Ziyan Jiang; Ningyuan Zhao; Shumin Deng; Huajun Chen; Ningyu Zhang
Venue: ACL 2025 (Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025))

Abstract: This paper presents the ZJUKLAB team’s submission for {emph{SemEval-2025 Task 4: Unlearning Sensitive Content from Large Language Models}}. This task aims to selectively erase sensitive knowledge from large language models, avoiding both over-forgetting and under-forgetting issues. We propose an unlearning system that leverages Model Merging (specifically TIES-Merging), combining two specialized models into a more balanced unlearned model.Our system achieves competitive results, ranking {textbf{second among 26 teams}}, with an online score of 0.944 for Task Aggregate and 0.487 for overall Aggregate. In this paper, we also conduct local experiments and perform a comprehensive analysis of the unlearning process, examining performance trajectories, loss dynamics, and weight perspectives, along with several supplementary experiments, to understand the effectiveness of our method.Furthermore, we analyze the shortcomings of our method and evaluation metrics, emphasizing that MIA scores and ROUGE-based metrics alone are insufficient to fully evaluate successful unlearning. Finally, we emphasize the need for more comprehensive evaluation methodologies and rethinking of unlearning objectives in future research.
GPLSICORTEX at SemEval-2025 Task 10: Leveraging Intentions for Generating Narrative Extractions
Authors: Ivan Martinez - Murillo; María Miró Maestre; Aitana Martínez; Snorre Ralund; Elena Lloret; Paloma Moreda; Armando Suárez Cueto
Venue: ACL 2025 (Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025))

Abstract: This paper describes our approach to address the SemEval-2025 Task 10 subtask 3, which is focused on narrative extraction given news articles with a dominant narrative. We design an external knowledge injection approach to fine-tune a Flan-T5 model so the generated narrative explanations are in line with the dominant narrative determined in each text. We also incorporate pragmatic information in the form of communicative intentions, using them as external knowledge to assist the model. This ensures that the generated texts align more closely with the intended explanations and effectively convey the expected meaning. The results show that our approach ranks 3rd in the task leaderboard (0.7428 in Macro-F1) with concise and effective news explanations. The analyses highlight the importance of adding pragmatic information when training systems to generate adequate narrative extractions.
MRS at SemEval-2025 Task 11: A Hybrid Approach for Bridging the Gap in Text-Based Emotion Detection
Authors: Milad Afshari; Richard Frost; Samantha Kissel; Kristen Johnson
Venue: ACL 2025 (Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025))

Abstract: We tackle the challenge of multi-label emotion detection in short texts, focusing on SemEval-2025 Task 11 Track A. Our approach, RoEmo, combines generative and discriminative models in an ensemble strategy to classify texts into five emotions: anger, fear, joy, sadness, and surprise.The generative model, instruction-finetuned on emotion detection datasets, undergoes additional fine-tuning on the SemEval-2025 Task 11 Track A dataset to enhance its performance for this specific task. Meanwhile, the discriminative model, based on binary classification, offers a straightforward yet effective approach to classification.We review recent advancements in multi-label emotion detection and analyze the task dataset. Our results show that RoEmo ranks among the top-performing systems, demonstrating high accuracy and reliability.
cocoa at SemEval-2025 Task 10: Prompting vs. Fine-Tuning: A Multilevel Approach to Propaganda Classification
Authors: Vineet Saravanan; Steven Wilson
Venue: ACL 2025 (Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025))

Abstract: The increasing sophistication of natural language processing models has facilitated advancements in hierarchical text classification, particularly in the domain of propaganda detection. This paper presents our submission to SemEval 2025 Task 10, Subtask 1, which focuses on multilevel text classification for identifying and categorizing propaganda narratives in online news. We investigate two primary approaches: (1) prompt-based classification using large language models (LLMs) like GPT, which offers flexibility but struggles with hierarchical categorization, and (2) fine-tuning transformer-based models, where we employ a hierarchical structure—one model classifies the main propaganda category, followed by three separate models specializing in subcategory classification. Our results indicate that while LLMs demonstrate some generalization ability, fine-tuned models significantly outperform them in accuracy and reliability, reinforcing the importance of task-specific supervised learning for propaganda detection. Additionally, we discuss challenges related to data sparsity in subclassification and explore potential enhancements such as multi-task learning and hierarchical loss functions. Our findings contribute to the broader field of automated propaganda detection and emphasize the value of structured classification models in combating misinformation. All code and data used in our experiments will be made publicly available on our GitHub
CICL at SemEval-2025 Task 9: A Pilot Study on Different Machine Learning Models for Food Hazard Detection Challenge
Authors: Weiting Wang; Wanzhao Zhang
Venue: ACL 2025 (Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025))

Abstract: This paper describes our approaches to SemEval-2025 task 9, a multiclass classification task to detect food hazards and affected products, given food incident reports from web resources. The training data consists of the date of the incidents and the text of the incident reports, as well as the labels: “hazard-category” and “product-category” for task 1, “hazard” and “product” for task 2. We primarily focused on solving task 1 of this challenge. Our approach is in two directions: Firstly, we fine-tuned BERT-based models (BERT and ModernBERT); secondly, in addition to BERT-based models, linearSVC, random forest classifier, and LightGBM were also used to tackle the challenge. From the experiment, we have learned that BERT-based models outperformed the other models mentioned above, and applying focal loss to BERT-based models optimized their performance on imbalanced classification tasks.
Hallucination Detectives at SemEval-2025 Task 3: Span-Level Hallucination Detection for LLM-Generated Answers
Authors: Passant Elchafei; Mervat Abu - Elkheir
Venue: ACL 2025 (Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025))

Abstract: Detecting spans of hallucination in LLM-generated answers is crucial for improving factual consistency. This paper presents a span-level hallucination detection framework for the SemEval-2025 Shared Task, focusing on English and Arabic texts. our approach integrates Semantic Role Labeling (SRL) to decompose the answer into atomic roles, which are then compared with a retrieved reference context obtained via question-based LLM prompting. Using a DeBERTa-based textual entailment model, we evaluate each role’s semantic alignment with the retrieved context. The entailment scores are further refined through token-level confidence measures derived from output logits, and the combined scores are used to detect hallucinated spans. Experiments on the Mu-SHROOM dataset demonstrate competitive performance. Additionally, hallucinated spans have been verified through fact-checking by prompting GPT-4 and LLaMA. Our findings contribute to improving hallucination detection in LLM-generated responses.
iLostTheCode at SemEval-2025 Task 10: Bottom-up Multilevel Classification of Narrative Taxonomies
Authors: Lorenzo Vittorio Concas; Manuela Sanguinetti; Maurizio Atzori
Venue: ACL 2025 (Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025))

Abstract: This paper describes the approach used to address the task of narrative classification, which has been proposed as a subtask of Task 10 on Multilingual Characterization and Extraction of Narratives from Online News at the SemEval 2025 campaign. The task consists precisely in assigning all relevant sub-narrative labels from a two-level taxonomy to a given news article in multiple languages (i.e., Bulgarian, English, Hindi, Portuguese and Russian). This involves performing both multi-label and multi-class classification. The model developed for this purpose uses multiple pretrained BERT-based models to create contextualized embeddings that are concatenated and then fed into a simple neural network to compute classification probabilities. Results on the official test set, evaluated using samples $F_1$, range from $0.15$ in Hindi (rank #9) to $0.41$ in Russian (rank #3). Besides an overview of the system and the results obtained in the task, the paper also includes some additional experiments carried out after the evaluation phase along with a brief discussion of the observed errors.
Pixel Phantoms at SemEval-2025 Task 11: Enhancing Multilingual Emotion Detection with a T5 and mT5-Based Approach
Authors: Jithu Morrison S; Janani Hariharakrishnan; Harsh Pratap Singh
Venue: ACL 2025 (Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025))

Abstract: Emotion recognition in textual data is a crucial NLP task with applications in sentiment analysis and mental health monitoring. SemEval 2025 Task 11 introduces a multilingual dataset spanning 28 languages, including low-resource ones, to improve cross-lingual emotion detection. Our approach utilizes T5 for English and mT5 for other languages, fine-tuning them for multi-label classification and emotion intensity estimation. Our findings demonstrate the effectiveness of transformer-based models in capturing nuanced emotional expressions across diverse languages.
TableWise at SemEval-2025 Task 8: LLM Agents for TabQA
Authors: Harsh Bansal; Aman Raj; Akshit Sharma; Parameswari Krishnamurthy
Venue: ACL 2025 (Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025))

Abstract: Tabular Question Answering (TabQA) is a challenging task that requires models to comprehend structured tabular data and generate accurate responses based on complex reasoning. In this paper, we present our approach to SemEval Task 8: Tabular Question Answering, where we develop a large language model (LLM)-based agent capable of understanding and reasoning over tabular inputs. Our agent leverages a hybrid retrieval and generation strategy, incorporating structured table parsing, semantic understanding, and reasoning mechanisms to enhance response accuracy. We fine-tune a pre-trained LLM on domain-specific tabular data, integrating chain-of-thought prompting and adaptive decoding to improve multi-hop reasoning over tables. Experimental results demonstrate that our approach achieves competitive performance, effectively handling numerical operations, entity linking, and logical inference. Our findings suggest that LLM-based agents, when properly adapted, can significantly advance the state of the art in tabular question answering.
madhans476 at SemEval-2025 Task 9: Multi-Model Ensemble and Prompt-Based Learning for Food Hazard Prediction
Authors: Madhan S; Gnanesh R; Gopal D; Sunil Saumya
Venue: ACL 2025 (Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025))

Abstract: This paper presents a hybrid approach to food hazard detection for SemEval-2025 Task 9, combining traditional machine learning with advanced language models. For hazard classification (Sub-Task 1), we implemented a novel ensemble system integrating XGBoost with fine-tuned GPT-2 Large and LLaMA 3.1 1B models. For vector detection (Sub-Task 2), we employed a prompt-engineered approach using Flan-T5-XL, highlighting challenges in exact vector matching. Our analysis demonstrates the effectiveness of combining complementary models while revealing opportunities for improvement in rare category detection and extraction precision.
UniBuc-AE at SemEval-2025 Task 7: Training Text Embedding Models for Multilingual and Crosslingual Fact-Checked Claim Retrieval
Authors: Alexandru Enache
Venue: ACL 2025 (Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025))

Abstract: This paper describes our approach to the SemEval-2025 Task 7: Multilingual and Crosslingual Fact-Checked Claim Retrieval on both the monolingual and crosslingual tracks. Our training methodology for text embedding models combines contrastive pre-training and hard negatives mining in order to fine-tune models from the E5 family. Additionally, we introduce a novel approach for merging the results from multiple models by finding the best majority vote weighted configuration for each subtask using the validation dataset. Our team ranked 6th in the monolingual track scoring a 0.934 S@10 averaged over all languages and achieved a 0.79 S@10 on the crosslingual task, ranking 8th in this track.
GT-NLP at SemEval-2025 Task 11: EmoRationale, Evidence-Based Emotion Detection via Retrieval-Augmented Generation
Authors: Daniel Saeedi; Alireza Kheirandish; Sirwe Saeedi; Hossein Sahour; Aliakbar Panahi; Iman Naeeni
Venue: ACL 2025 (Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025))

Abstract: Emotion detection in multilingual settings presents significant challenges, particularly for low-resource languages where labeled datasets are scarce. To address these limitations, we introduce EmoRationale, a Retrieval-Augmented Generation (RAG) framework designed to enhance explainability and cross-lingual generalization in emotion detection. Our approach combines vector-based retrieval with in-context learning in large language models (LLMs), using semantically relevant examples to enhance classification accuracy and interpretability. Unlike traditional fine-tuning methods, our system provides evidence-based reasoning for its predictions, making emotion detection more transparent and adaptable across diverse linguistic contexts. Experimental results on the SemEval-2025 Task 11 dataset demonstrate that our RAG-based method achieves strong performance in multi-label emotion classification, emotion intensity assessment, and cross-lingual emotion transfer, surpassing conventional models in interpretability while remaining cost-effective.
STFXNLP at SemEval-2025 Task 11 Track A: Neural Network, Schema, and Next Word Prediction-based Approaches to Perceived Emotion Detection
Authors: Noah Murrant; Samantha Brooks; Milton King
Venue: ACL 2025 (Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025))

Abstract: In this work, we discuss our models that were applied to the SemEval-2025 Task 11: Bridging the Gap in Text-Based Emotion Detection (Muhammad et al., 2025b). We focused on the English data set of track A, which involves determining what emotions the reader of a snippet of text is feeling. We applied three different types of models that vary in their approaches and reported our findings on the task’s test set. We found that the performance of our models differed from each other, but neither of our models outperformed the task’s baseline model.
LATE-GIL-nlp at Semeval-2025 Task 10: Exploring LLMs and transformers for Characterization and extraction of narratives from online news
Authors: Ivan Diaz; Fredin Vázquez; Christian Luna; Aldair Conde; Gerardo Sierra; Helena Gómez - Adorno; Gemma Bel - Enguix
Venue: ACL 2025 (Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025))

Abstract: This paper tackles SemEval~2025 Task~10, “Multilingual Characterization and Extraction of Narratives from Online News,” focusing on the Ukraine-Russia War and Climate Change domains. Our approach covers three subtasks: (1) {textbf{Entity Framing}}, assigning protagonist-antagonist-innocent roles with a prompt-based Llama~3.1~(8B) method; (2) {textbf{Narrative Classification}}, a multi-label classification using XLM-RoBERTa-base; and (3) {textbf{Narrative Extraction}}, generating concise, text-grounded explanations via FLAN-T5. Results show a unified multilingual transformer pipeline, combined with targeted preprocessing and fine-tuning, achieves substantial gains over baselines while effectively capturing complex narrative structures despite data imbalance and varied label distributions.
LATE-GIL-NLP at SemEval-2025 Task 11: Multi-Language Emotion Detection and Intensity Classification Using Transformer Models with Optimized Loss Functions for Imbalanced Data
Authors: Jesús V á z q u e z - O s o r i o; Helena Gómez - Adorno; Gerardo Sierra; Vladimir Sierra - Casiano; Diana Canchola - Hernández; José Tovar - Cortés; Roberto Solís - Vilchis; Gabriel Salazar
Venue: ACL 2025 (Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025))

Abstract: This paper addresses our approach to Task 11 (Track A and B) at the SemEval-2025, which focuses on the challenge of multilingual emotion detection in text, specifically identifying perceived emotions. The task is divided into tracks, we participated in two tracks: Track A, involving multilabel emotion detection, and Track B, which extends this to predicting emotion intensity on an ordinal scale. Addressing the challenges of imbalanced data and linguistic diversity, we propose a robust approach using pre-trained language models, fine-tuned with techniques such as extensive and deep hyperparameter optimization, along with loss function combinations to improve performance on imbalanced datasets and underrepresented languages. Our results demonstrate strong performance on Track A, particularly in low-resource languages such as Tigrinya (ranked 2nd), Igbo (ranked 3rd), and Oromo (ranked 4th). This work offers a scalable framework for emotion detection with applications in cross-cultural communication and human-computer interaction.