GenAI4ELab/papercli-papers huggingface dataset:
Tue-JMS at SemEval-2025 Task 11: KReLax: An Ensemble-Based Approach for Multilingual Emotion Detection and Addressing Data Imbalance
Authors: Jingyu Han; Megan Horikawa; Suvi Lehtosalo
Venue: ACL 2025 (Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025))

Abstract: Emotion detection research has primarily focused on English, leaving a gap for low-resource languages. To address this, we present KReLaX, a multilingual ensemble model for multi-label emotion detection, combining three BERT-based encoders with a weighted voting layer. Within the shared task, our system performed well in multi-label classification, ranking 3rd in Tatar and achieving strong results in Hindi, Russian, Marathi, and Spanish. In emotion intensity classification, we achieved 6th place in Amharic and Hausa. While our system struggled in the zero-shot track, it achieved 7th place in Indonesian. These results highlight both the potential and the challenges of multilingual emotion detection, emphasizing the need for improved generalization in low-resource settings.
QUST_NLP at SemEval-2025 Task 7: A Three-Stage Retrieval Framework for Monolingual and Crosslingual Fact-Checked Claim Retrieval
Authors: Youzheng Liu; Jiyan Liu; Xiaoman Xu; Taihang Wang; Yimin Wang; Ye Jiang
Venue: ACL 2025 (Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025))

Abstract: This paper describes the participation of team QUST_NLP in the SemEval-2025 Task 7. We propose a three-stage retrieval framework specifically designed for fact-checked claim retrieval. Initially, we evaluate the performance of several retrieval models and select the one that yields the best results for candidate retrieval. Next, we employ multiple re-ranking models to enhance the candidate results, with each model selecting the Top-10 outcomes. In the final stage, we utilize weighted voting to determine the final retrieval outcomes. Our approach achieved 5th place in the monolingual track and 7th place in the crosslingual track. We release our system code at: https://github.com/warmth27/SemEval2025_Task7.
CCNU at SemEval-2025 Task 8: Enhancing Question Answering on Tabular Data with Two-Stage Corrections
Authors: Chenlian Zhou; Xilu Cai; Yajuan Tong; Chengzhao Wu; Xin Xu; Guanyi Chen; Tingting He
Venue: ACL 2025 (Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025))

Abstract: We present the system developed by the Central China Normal University (CCNU) team for the SemEval-2025 shared task 8, which focuses on Question-Answering (QA) for tabular data. Our approach leverages multiple Large Language Models (LLMs), conducting tabular QA as code completion. Additionally, to improve its reliability, we introduce a two-stage corrections mechanism, in which we instruct the LLM to correct the code according to the judges of whether the code is executable and whether the answer obtained from executing the code is semantically consistent with the question. The experiment demonstrates that code correction works but answer correction does not. Finally, we discuss other unsuccessful approaches explored during our development process.
HalluRAG-RUG at SemEval-2025 Task 3: Using Retrieval-Augmented Generation for Hallucination Detection in Model Outputs
Authors: Silvana Abdi; Mahrokh Hassani; Rosalien Kinds; Timo Strijbis; Roman Terpstra
Venue: ACL 2025 (Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025))

Abstract: Large Language Models (LLMs) suffer from a critical limitation: hallucinations, which refers to models generating fluent but factually incorrect text. This paper presents our approach to hallucination detection in English model outputs as part of the SemEval-2025 Task 3 (Mu-SHROOM). Our method, HalluRAG-RUG, integrates Retrieval-Augmented Generation (RAG) using Llama-3 and prediction models using token probabilities and semantic similarity. We retrieved relevant factual information using a named entity recognition (NER)-based Wikipedia search and applied abstractive summarization to refine the knowledge base. The hallucination detection pipeline then used this retrieved knowledge to identify inconsistent spans in model-generated text. This result was combined with the results of two systems which identified hallucinations based on token probabilities and low-similarity sentences. Our system placed 33rd out of 41, performing slightly below the ‘mark all’ baseline but surpassing the ‘mark none’ and ‘neural’ baselines with an IoU of 0.3093 and a correlation of 0.0833.
SALT at SemEval-2025 Task 2: A SQL-based Approach for LLM-Free Entity-Aware-Translation
Authors: Tom Völker; Jan Pfister; Andreas Hotho
Venue: ACL 2025 (Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025))

Abstract: Entity-aware machine translation faces significant challenges when translating culturally-adapted named entities that require knowledge beyond the source text. We present SALT (SQL-based Approach for LLM-Free Entity-Aware-Translation), a parameter-efficient system for the SemEval-2025 Task 2. Our approach combines SQL-based entity retrieval with constrained neural translation via logit biasing and explicit entity annotations. Despite its simplicity, it achieves state-of-the-art performance (First Place) among approaches not using gold-standard data, while requiring far less computation than LLM-based methods. Our ablation studies show simple SQL-based retrieval rivals complex neural models, and strategic model refinement outperforms increased model complexity. SALT offers an alternative to resource-intensive LLM-based approaches, achieving comparable results with only a fraction of the parameters.
AlexNLP-MO at SemEval-2025 Task 8: A Chain of Thought Framework for Question-Answering over Tabular Data
Authors: Omar Mokhtar; Minah Ghanem; Nagwa El - Makky
Venue: ACL 2025 (Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025))

Abstract: Table Question Answering (TQA) involves extracting answers from structured data using natural language queries, a challenging task due to diverse table formats and complex reasoning. This work develops a TQA system using the DataBench dataset, leveraging large language models (LLMs) to generate Python code in a zero-shot manner. Our approach is highly generic, relying on a structured Chain-of-Thought framework to improve reasoning and data interpretation. Experimental results demonstrate that our method achieves high accuracy and efficiency, making it a flexible and effective solution for real-world tabular question answering.
Team UBD at SemEval-2025 Task 11: Balancing Class and Task Importance for Emotion Detection
Authors: Cristian Paduraru
Venue: ACL 2025 (Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025))

Abstract: This article presents the systems used by Team UBD in Task 11 of SemEval-2025. We participated in all three sub-tasks, namely Emotion Detection, Emotion Intensity Estimation and Cross-Lingual Emotion Detection. In our solutions we make use of publicly available Language Models (LMs) already fine-tuned for the Emotion Detection task, as well as open-sourced models for Neural Machine Translation (NMT). We robustly adapt the existing LMs to the new data distribution, balance the importance of all emotions and classes and also use a custom sampling scheme.We present fine-grained results in all sub-tasks and analyze multiple possible sources for errors for the Cross-Lingual Emotion Detection sub-task.
HausaNLP at SemEval-2025 Task 2: Entity-Aware Fine-tuning vs. Prompt Engineering in Entity-Aware Machine Translation
Authors: Abdulhamid Abubakar; Hamidatu Abdulkadir; Rabiu Ibrahim; Abubakar Auwal; Ahmad Wali; Amina Umar; Maryam Bala; Sani Abdullahi Sani; Ibrahim Said Ahmad; Shamsuddeen Hassan Muhammad; Idris Abdulmumin; Vukosi Marivate
Venue: ACL 2025 (Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025))

Abstract: This paper presents our findings for SemEval 2025 Task 2, a shared task on entity-aware machine translation (EA-MT). The goal of this task is to develop translation models that can accurately translate English sentences into target languages, with a particular focus on handling named entities, which often pose challenges for MT systems. The task covers 10 target languages with English as the source. In this paper, we describe the different systems we employed, detail our results, and discuss insights gained from our experiments.
Trans-Sent at SemEval-2025 Task 11: Text-based Multi-label Emotion Detection using Pre-Trained BERT Transformer Models
Authors: Zafar Sarif; Md Sharib Akhtar; Abhishek Das; Dipankar Das
Venue: ACL 2025 (Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025))

Abstract: We have introduced Trans-Sent, a Transformer-based model designed for multi-label emotion classification in SemEval-2025 Task 11. The model predicts perceived emotions such as joy, sadness, anger, fear, surprise, and disgust from text across seven languages, including Amharic, German, English, Hindi, Marathi, Russian, and Romanian. To handle data imbalance, the system incorporates preprocessing techniques, SMOTE oversampling, and feature engineering to enhance classification accuracy. The model was trained using the BRIGHTER and EthioEmo datasets, which contain diverse textual sources, such as social media, news, literature, and personal narratives. Traditional machine learning models, including Logistic Regression and Decision Trees, were tested but proved inadequate for multi-label classification due to their limited ability to capture contextual and semantic meaning. Fine-tuned BERT models demonstrated superior performance, with Russian achieving the highest ranking (9th overall), while languages with complex grammar, such as German and Amharic, performed lower. Future enhancements may include advanced data augmentation, cross-lingual learning, and multimodal emotion analysis to improve classification across different languages. Trans-Sent contributes to NLP by advancing multi-label emotion detection, particularly in underrepresented languages.
KostasThesis2025 at SemEval-2025 Task 10 Subtask 2: A Continual Learning Approach to Propaganda Analysis in Online News
Authors: Konstantinos Eleftheriou; Panos Louridas; John Pavlopoulos
Venue: ACL 2025 (Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025))

Abstract: In response to the growing challenge of propagandistic presence through online media inonline news, the increasing need for automated systems that are able to identify and classify narrative structures in multiple languages is evident. We present our approach to the SemEval-2025 Task 10 Subtask 2, focusing on the challenge of hierarchical multi-label, multi-class classification in multilingual news articles. We present methods to handle long articles with respect to how they are naturally structured in the dataset, propose a hierarchical classification neural network model with respect to the taxonomy, and a continual learning training approach that leverages cross-lingual knowledge transfer.
DUTJBD at SemEval-2025 Task 3: A Range of Approaches for Predicting Hallucination Generation in Models
Authors: Shengdi Yin; Zekun Wang; Liang Yang; Hongfei Lin
Venue: ACL 2025 (Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025))

Abstract: This paper details the various methods we explored.Thank you.
BrightCookies at SemEval-2025 Task 9: Exploring Data Augmentation for Food Hazard Classification
Authors: Foteini Papadopoulou; Osman Mutlu; Neris Özen; Bas Van Der Velden; Iris Hendrickx; Ali Hurriyetoglu
Venue: ACL 2025 (Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025))

Abstract: This paper presents our system developed for the SemEval-2025 Task 9: The Food Hazard Detection Challenge. The shared task’s objective is to evaluate explainable classification systems for classifying hazards and products in two levels of granularity from web-collected food recall incident reports. In this work, we propose text augmentation techniques as a way to improve poor performance in minority classes and compare their effect for each category on various transformer and machine learning models. We apply three word-level data augmentation techniques, namely synonym replacement, random word swapping, and contextual word insertion utilizing BERT. The resultsshow that transformer models tend to have a better overall performance. Meanwhile, a statistically significant improvement (P 0.05) was observed in the fine-grained categories when using BERT to compare the baseline model with the three augmented models, which achieved a 6% increase in correct predictions for minority hazard classes. This suggests that targeted augmentation of minority classes can improve the performance of transformer models.
Zhoumou at SemEval-2025 Task 1: Leveraging Multimodal Data Augmentation and Large Language Models for Enhanced Idiom Understanding
Authors: Yingzhou Zhao; Bowen Guan; Liang Yang; Hongfei Lin
Venue: ACL 2025 (Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025))

Abstract: This paper elaborates on my task content regarding Semeval 2025 Task 1 Subtask A. Please refer to it.
TabaQA at SemEval-2025 Task 8: Column Augmented Generation for Question Answering over Tabular Data
Authors: Ekaterina Antropova; Egor Kratkov; Roman Derunets; Margarita Trofimova; Ivan Bondarenko; Alexander Panchenko; Vasily Konovalov; Maksim Savkin
Venue: ACL 2025 (Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025))

Abstract: The DataBench shared task in the SemEval-2025 competition aims to tackle the problem of QA from data in tables. Given the diversity of the structure of tables, there are different approaches to retrieving the answer. Although Retrieval-Augmented Generation (RAG) is a viable solution, extracting relevant information from tables remains challenging. In addition, the table can be prohibitively large for direct integration into the LLM context. In this paper, we address QA over tabular data first by identifying relevant columns that might contain the answers, then the LLM generates answers by providing the context of the relevant columns, and finally, the LLM refines its answers. This approach secured us 7th place in the DataBench lite category.
NBF at SemEval-2025 Task 5: Light-Burst Attention Enhanced System for Multilingual Subject Recommendation
Authors: Baharul Islam; Nasim Ahmad; Ferdous Barbhuiya; Kuntal Dey
Venue: ACL 2025 (Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025))

Abstract: This paper presents a system for automated subject tagging in a bilingual academic setting. Our approach leverages a novel burst attention mechanism to enhance the alignment between article and subject embeddings, derived from a large cross-lingual subject corpus. By employing a margin-based loss with negative sampling, our resource-efficient model achieves competitive performance in both quantitative and qualitative evaluations. Experimental results demonstrate average recall rates of 32.24% on the full test set, along with robust performance on specialized subsets, making our system well-suited for large-scale subject recommendation tasks.
QMUL at SemEval-2025 Task 11: Explicit Emotion Detection with EmoLex, Feature Engineering, and Threshold-Optimized Multi-Label Classification
Authors: Angeline Wang; Aditya Gupta; Iran Roman; Arkaitz Zubiaga
Venue: ACL 2025 (Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025))

Abstract: SemEval 2025 Task 11 Track A explores the detection of multiple emotions in text samples. Our best model combined BERT (fine-tuned on an emotion dataset) predictions and engineered features with EmoLex words appended. Together, these were used as input to train a multi-layer perceptron. This achieved a final test set Macro F1 score of 0.56. Compared to only using BERT predictions, our system improves performance by 43.6%.
Team INSALyon2 at SemEval-2025 Task 10: A Zero-shot Agentic Approach to Text Classification
Authors: Mohamed - Nour Eljadiri; Diana Nurbakova
Venue: ACL 2025 (Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025))

Abstract: We present Team INSALyon2’s agentic approach to SemEval-2025 Task 10 Subtask 2, which focuses on the multi-label classification of narratives in news articles across five languages. Our system employs a zero-shot architecture where specialized Large Language Model (LLM) agents handle binary classification tasks for individual narrative/subnarrative labels, with a meta-agent aggregating these decisions into final multi-label predictions. Instead of fine-tuning on the dataset, we leverage AutoGen to orchestrate multiple GPT-based agents, each responsible for detecting specific narrative/subnarrative types in a modular framework. This agent-based approach naturally handles the challenge of multi-label classification by enabling parallel decisions across the two-level taxonomy. Experiments on the English subset demonstrate strong performance with our system achieving F1_macro_coarse = 0.513, F1_sample = 0.406, securing third place in the competition. Our findings show that zero-shot agentic approaches can be competitive in complex classification tasks.
Team INSAntive at SemEval-2025 Task 10: Hierarchical Text Classification using BERT
Authors: Yutong Wang; Diana Nurbakova; Sylvie Calabretto
Venue: ACL 2025 (Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025))

Abstract: In this paper, we propose a BERT-based hierarchical text classification framework to address the challenges of training multi-level classification tasks. As part of the SemEval-2025 Task 10 challenge (Subtask 2), the framework performs fine-grained text classification by training dedicated sub-category classifiers for each top-level category. Experimental results demonstrate the feasibility of the proposed approach in multi-class text classification tasks.
MSA at SemEval-2025 Task 3: High Quality Weak Labeling and LLM Ensemble Verification for Multilingual Hallucination Detection
Authors: Baraa Hikal; Ahmed Nasreldin; Ali Hamdi
Venue: ACL 2025 (Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025))

Abstract: This paper describes our submission for SemEval-2025 Task 3: Mu-SHROOM, the Multilingual Shared-task on Hallucinations and Related Observable Overgeneration Mistakes. The task involves detecting hallucinated spans in text generated by instruction-tuned Large Language Models (LLMs) across multiple languages. Our approach combines task-specific prompt engineering with an LLM ensemble verification mechanism, where a primary model extracts hallucination spans and three independent LLMs adjudicate their validity through probability-based voting. This framework simulates the human annotation workflow used in the shared task validation and test data. Additionally, a fuzzy matching algorithm is utilized to improve span alignment. Our system ranked 1st in Arabic and Basque, 2nd in German, Swedish, and Finnish, and 3rd in Czech, Farsi, and French.
SRCB at SemEval-2025 Task 9: LLM Finetuning Approach based on External Attention Mechanism in The Food Hazard Detection
Authors: Yuming Zhang; Hongyu Li; Yongwei Zhang; Shanshan Jiang; Bin Dong
Venue: ACL 2025 (Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025))

Abstract: This paper reports on the performance of SRCB’s system in SemEval-2025 Task 9: The Food Hazard Detection Challenge. We develop a system in the form of a pipeline consisting of two parts: 1. Candidate Recall Module, which selects the most probable correct labels from a large number of labels based on BERT model; 2. LLM Prediction Module, which is used to generate the final prediction based on Large Language Models(LLM). Additionally, to address the issue of long prompts caused by an excessive number of labels, we propose a model architecture to reduce resource consumption and improve performance. Our submission achieves the macro-F1 score of 80.39 on Sub-Task 1 and the macro-F1 score of 54.73 on Sub-Task 2. Our system is released at https://github.com/Doraxgui/Document_Attention
Emotion Train at SemEval-2025 Task 11: Comparing Generative and Discriminative Models in Emotion Recognition
Authors: Anastasiia Demidova; Injy Hamed; Teresa Lynn; Thamar Solorio
Venue: ACL 2025 (Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025))

Abstract: The emotion recognition task has become increasingly popular as it has a wide range of applications in many fields, such as mental health, product management, and population mood state monitoring. SemEval 2025 Task 11 Track A framed the emotion recognition problem as a multi-label classification task. This paper presents our proposed system submissions in the following languages: English, Algerian and Moroccan Arabic, Brazilian and Mozambican Portuguese, German, Spanish, Nigerian-Pidgin, Russian, and Swedish. Here, we compare the emotion-detecting abilities of generative and discriminative pre-trained language models, exploring multiple approaches, including curriculum learning, in-context learning, and instruction and few-shot fine-tuning. We also propose an extended architecture method with a feature fusion technique enriched with emotion scores and a self-attention mechanism. We find that BERT-based models fine-tuned on data of a corresponding language achieve the best results across multiple languages for multi-label text-based emotion classification, outperforming both baseline and generative models.
uir-cis at SemEval-2025 Task 3: Detection of Hallucinations in Generated Text
Authors: Jia Huang; Shuli Zhao; Yaru Zhao; Tao Chen; Weijia Zhao; Hangui Lin; Yiyang Chen; Binyang Li
Venue: ACL 2025 (Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025))

Abstract: The widespread deployment of large language models (LLMs) across diverse domains has underscored the critical need to ensure the credibility and accuracy of their generated content, particularly in the presence of hallucinations. These hallucinations can severely compromise both the practical performance of models and the security of their applications. In response to this issue, SemEval-2025 Task 3 Mu-SHROOM: Multilingual Shared-task on Hallucinations and Related Observable Overgeneration Mistakes introduces a more granular task for hallucination detection. This task seeks to identify hallucinations in text, accurately locate hallucinated segments, and assess their credibility. In this paper, we present a three-stage method for fine-grained hallucination detection and localization. First, we transform the text into a triplet representation, facilitating more precise hallucination analysis. Next, we leverage a large language model to generate fact-reference texts that correspond to the triplets. Finally, we employ a fact alignment strategy to identify and localize hallucinated segments by evaluating the semantic consistency between the extracted triplets and the generated reference texts. We evaluate our method on the unlabelled test set across all languages in Task 3, demonstrating strong detection performance and validating its effectiveness in multilingual contexts.