GenAI4ELab/papercli-papers huggingface dataset:
HTU at SemEval-2025 Task 11: Divide and Conquer - Multi-Label emotion classification using 6 DziriBERTs submodels with Label-fused Iterative Mask Filling technique for low-resource data augmentation.
Authors: Abdallah Saleh; Mariam Biltawi
Venue: ACL 2025 (Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025))

Abstract: In this paper, the authors address the challenges of multi-label emotion detection in the Algerian dialect by proposing a novel Label-fused Iterative Mask Filling (L-IMF) data augmentation technique combined with a multi-model architecture. The approach leverages DziriBERT, a BERT variant pre-trained on Algerian text, to generate contextually and label-sensitive aug- mented data, mitigating class imbalance while preserving label consistency. The proposed method uses six independent classifiers, each trained on balanced datasets for dedicated la- bel, to improve performance. The results show significant improvement on mutli-label classification task using Deep Learning, with an F1 macro score of 0.536 on the validation dataset and 0.486 on the test dataset, the sys- tem ranked 28/41 on the Algerian dialect score- board; which is more than 7% higher than the task baseline using RemBERT.
BlueToad at SemEval-2025 Task 3: Using Question-Answering-Based Language Models to Extract Hallucinations from Machine-Generated Text
Authors: Michiel Pronk; Ekaterina Kamyshanova; Thijmen Adam; Maxim Van Der Maesen De Sombreff
Venue: ACL 2025 (Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025))

Abstract: Hallucination in machine-generated text poses big risks in various domains, such as finance, medicine, and engineering. Task 3 of SemEval-2025, Mu-SHROOM, challenges participants to detect hallucinated spans in such text. Our approach uses pre-trained language models and fine-tuning strategies to enhance hallucination spam detection, focusing on the English track. Firstly, we applied GPT-4o mini to generate synthetic data by labeling unlabeled data. Then, we employed encoder-only pre-trained language models with a question-answering architecture for hallucination span detection, ultimately choosing XLM-RoBERTa for fine-tuning on multilingual data. This model appeared to be our best and ranked 18th and 22nd on the English track with 0.469 intersection-over-union and 0.441 correlation scores, respectively. It achieved promising results across multiple languages, surpassing baseline methods in 11 out of 13 languages, with Hindi having the highest scores of 0.645 intersection-over-union and 0.684 correlation coefficient. Our findings highlight the potential of a QA approach and using synthetic and multilingual data for hallucination span detection.
IASBS at SemEval-2025 Task 11: Ensembling Transformers for Bridging the Gap in Text-Based Emotion Detection
Authors: Mehrzad Tareh; Erfan Mohammadzadeh; Aydin Mohandesi; Ebrahim Ansari
Venue: ACL 2025 (Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025))

Abstract: In this paper, we address the challenges of text-based emotion detection, focusing on multi-label classification, emotion intensity prediction, and cross-lingual emotion detection across various languages. We explore the use of advanced machine learning models, particularly transformers, in three tracks: emotion detection, emotion intensity prediction, and cross-lingual emotion detection. Our approach utilizes pre-trained transformer models, such as Gemini, DeBERTa, M-BERT, and M-DistilBERT, combined with techniques like majority voting and average ensemble voting (AEV) to enhance performance. We also incorporate multilingual strategies and prompt engineering to effectively handle the complexities of emotion detection across diverse linguistic and cultural contexts. Our findings demonstrate the success of ensemble methods and multilingual models in improving the accuracy and generalization of emotion detection, particularly for low-resource languages.
SBU-NLP at SemEval-2025 Task 8: Self-Correction and Collaboration in LLMs for Tabular Question Answering
Authors: Rashin Rahnamoun; Mehrnoush Shamsfard
Venue: ACL 2025 (Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025))

Abstract: This paper explains the submission of the SBU-NLP team at SemEval-2025 Task 8: question-answering over tabular data. We present a novel algorithm for this task, aimed at systems capable of interpreting large tables and providing accurate answers to natural language queries. The evaluation uses the DataBench dataset, which covers a wide range of topics and reflects the complexity of real-world tabular data. Our approach incorporates a self-correction mechanism that iteratively refines LLM-generated code to address errors and prevent common mistakes. Additionally, a multi-LLM collaborative strategy is employed to generate answers, where responses from multiple LLMs are compared, and the majority consensus or a valid alternative is selected. The method relies exclusively on open-source models, avoiding costly processes like training or fine-tuning. Experimental results demonstrate that combining multiple LLMs with self-correction leads to significant performance improvements. However, challenges arise with list-based answers and responses involving multiple numerical, string, or boolean values, where further refinement is needed. The proposed simple system was among the top performers in both Subtask A and Subtask B among open-source models in the competition.
Duluth at SemEval-2025 Task 7: TF-IDF with Optimized Vector Dimensions for Multilingual Fact-Checked Claim Retrieval
Authors: Shujauddin Syed; Ted Pedersen
Venue: ACL 2025 (Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025))

Abstract: This paper presents our approach to the SemEval-2025 Task 7 on Multilingual and Crosslingual Fact-Checked Claim Retrieval. We implemented a TF-IDF-based retrieval system with experimentation on vector dimensions and tokenization strategies. Our best-performing configuration used word-level tokenization with a vocabulary size of 15,000 features, achieving an average success@10 score of 0.78 on the development set and 0.69 on the test set across ten languages. Our system showed stronger performance on higher resource languages with large performance gaps compared to the top-ranked system, which achieved 0.96 average success@10. Our findings suggest that though advanced neural architectures are increasingly dominant in multilingual retrieval tasks, properly optimized traditional methods like TF-IDF remain competitive baselines, especially in limited resource scenarios.
BitsAndBites at SemEval-2025 Task 9: Improving Food Hazard Detection with Sequential Multitask Learning and Large Language Models
Authors: Aurora Gensale; Irene Benedetto; Luca Gioacchini; Luca Cagliero; Alessio Bosca
Venue: ACL 2025 (Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025))

Abstract: Automatic and early detection of foodborne hazards is crucial for preventing outbreaks. Existing AI-based solutions often struggle with the complexity and noise of food recall reports and overcome the dependency between product and hazard labels. We introduce a methodology to classify reports on food-related incidents to address these challenges. Our approach leverages LLM-based information extraction to minimize report variability, alongside a two-stage classification pipeline. The first model assigns coarse-grained labels, narrowing the space of eligible fine-grained labels for the second model. This sequential process allows us to capture hierarchical label dependencies between products and hazards and their respective categories. Additionally, we design each model with two classification heads relying on the inherent relations between food products and associated hazards. We validate our approach on two multi-label classification sub-tasks. Experimental results demonstrate the effectiveness of our approach, achieving an improvement of +30% and +40% in classification performance compared to the baseline.
G-MACT at SemEval-2025 Task 8: Exploring Planning and Tool Use in Question Answering over Tabular Data
Authors: Wei Zhou; Mohsen Mesgar; Annemarie Friedrich; Heike Adel
Venue: ACL 2025 (Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025))

Abstract: This paper describes our system submitted to SemEval-2024 Task 8 “Question Answering over Tabular Data.”The shared task focuses on tackling real-life table question answering (TQA) involving extremely large tables with the additional challenges of interpreting complex questions. To address these issues, we leverage a framework of Multi-Agent Collaboration with Tool use (MACT), a method that combines planning and tool use. The planning module breaks down a complex question by designing a step-by-step plan. This plan is translated into Python code by a coding model, and a Python interpreter executes the code to generate an answer. Our system demonstrates competitive performance in the shared task and is ranked 5th out of 38 in the open-source model category. We provide a detailed analysis of our model, evaluating the effectiveness and the efficiency of each component, and identify common error patterns. Our paper offers essential insights and recommendations for future advancements in developing TQA systems.
UMUTeam at SemEval-2025 Task 1: Leveraging Multimodal and Large Language Model for Identifying and Ranking Idiomatic Expressions
Authors: Ronghao Pan; Tomás Bernal - Beltrán; José Antonio García - Díaz; Rafael Valencia - García
Venue: ACL 2025 (Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025))

Abstract: Idioms are non-compositional linguistic expressions whose meanings cannot be directly inferred from the individual words that compose them, posing significant challenges for natural language processing systems. This paper describes the participation of the UMUTeam in Subtask A of the AdMIRe shared task (SemEval 2025), which focuses on understanding idiomatic expressions through visual and contextual representations in English and Portuguese. Specifically, the task involves ranking a set of images according to how well they represent the sense of a potentially idiomatic nominal compound within a given contextual sentence. To address this challenge, we adopted a multimodal approach that combines textual and visual features using pre-trained language models, such as BERT and XLM-RoBERTa, along with Vision Transformers. Additionally, we explored the in-context learning capabilities of Large Language Models (LLMs), particularly Llama-3.1-8B, for image classification. These models are trained using a regression approach to rank images according to their semantic alignment with the contextual meaning of idioms. The results show that the Llama-3.1-8B model performs best for English, ranking 32 out of 36, while the XLM + ViT model is more effective for Portuguese, ranking 21 out of 24.
UMUTeam at SemEval-2025 Task 3: Detecting Hallucinations in Multilingual Texts Using Encoder-only Models Guided by Large Language Models
Authors: Ronghao Pan; Tomás Bernal - Beltrán; José Antonio García - Díaz; Rafael Valencia - García
Venue: ACL 2025 (Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025))

Abstract: Large Language Models like GPT-4, LLaMa, Mistral, and Gemma have revolutionized Natural Language Processing, advancing language comprehension, generation, and reasoning. However, they also present challenges, particularly the tendency to hallucinate—that is, to produce false or fabricated information. This paper presents our participation in Task 3 Mu-SHROOM of SemEval 2025, which focuses on detecting hallucinations in multilingual contexts. Specifically, the task requires identifying text segments generated by LLMs that correspond to hallucinations and calculating the hallucination probability for each character in the text. To address this challenge, we adopted a token classification approach using the pre-trained XLM-RoBERTa-large model, fine-tuned on the provided training set. Additionally, we integrated context from Llama-3.1-70B to enhance hallucination detection by leveraging its broader and more up-to-date knowledge base. Our approach combines the multilingual capability of XLM-RoBERTa with the contextual understanding of Llama-3.1-70B, producing a detailed hallucination probability for each character in the text. The results demonstrate that our approach consistently outperforms baseline methods across multiple languages, particularly in detecting token-level hallucinations.
UMUTeam at SemEval-2025 Task 7: Multilingual Fact-Checked Claim Retrieval with XLM-RoBERTa and Self-Alignment Pretraining Strategy
Authors: Ronghao Pan; Tomás Bernal - Beltrán; José Antonio García - Díaz; Rafael Valencia - García
Venue: ACL 2025 (Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025))

Abstract: In today’s digital age, the rapid dissemination of information through social networks poses significant challenges in verifying the veracity of shared content. The proliferation of misinformation can have serious consequences, influencing public opinion, policy decisions, and social dynamics. Fact-checking plays a critical role in countering misinformation; however, the manual verification process is time-consuming, especially when dealing with multilingual content. This paper presents our participation in the Multilingual and Crosslingual Fact-Checked Claim Retrieval task (SemEval 2025), which seeks to identify previously fact-checked claims relevant to social media posts. Our proposed system leverages XLM-RoBERTa, a multilingual Transformer model, combined with metric learning and hard negative mining strategies, to optimize the semantic comparison of posts and fact-checks across multiple languages. By fine-tuning a shared embedding space and employing a multiple similarity loss function, our approach enhances retrieval accuracy while maintaining efficiency. Evaluation results demonstrate competitive performance across multiple languages, reaching 25th place and highlighting the potential of multilingual NLP models in automating the fact-checking process and mitigating misinformation spread.
Lazarus NLP at SemEval-2025 Task 11: Fine-Tuning Large Language Models for Multi-Label Emotion Classification via Sentence-Label Pairing
Authors: Wilson Wongso; David Setiawan; Ananto Joyoadikusumo; Steven Limcorn
Venue: ACL 2025 (Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025))

Abstract: Multi-label emotion classification in low-resource languages remains challenging due to limited annotated data and model adaptability. To address this, we fine-tune large language models (LLMs) using a sentence-label pairing approach, optimizing efficiency while improving classification performance. Evaluating on Sundanese, Indonesian, and Javanese, our method outperforms conventional classifier-based fine-tuning and achieves strong zero-shot cross-lingual transfer. Notably, our approach ranks first in the Sundanese subset of SemEval-2025 Task 11 Track A. Our findings demonstrate the effectiveness of LLM fine-tuning for low-resource emotion classification, underscoring the importance of tailoring adaptation strategies to specific language families in multilingual contexts.
RSSN at SemEval-2025 Task 11: Optimizing Multi-Label Emotion Detection with Transformer-Based Models and Threshold Tuning
Authors: Ravindran V; Rajalakshmi Sivanaiah; Angel Deborah S
Venue: ACL 2025 (Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025))

Abstract: Our study explores multi-label emotion classification using fine-tuned BERT models, achieving superior performance over traditional methods such as logistic regression. The intricate nature of overlapping emotional expressions in text necessitates a robust classification framework. Fine-tuning BERT with weighted binary cross-entropy loss enhances predictive accuracy, particularly for underrepresented emotions like anger and joy. Moreover, threshold optimization plays a pivotal role in refining decision boundaries, boosting recall, and increasing the macro F1-score. Comparative analysis against RoBERTa and XGBoost further underscores the effectiveness of contextual embeddings in capturing subtle emotional nuances. Despite these improvements, challenges such as class imbalance and inter-class confusion persist, highlighting the need for future advancements in ensemble learning, contrastive pretraining, and domain-adaptive fine-tuning.
Modgenix at SemEval-2025 Task 1: Context Aware Vision Language Ranking (CAViLR) for Multimodal Idiomaticity Understanding
Authors: Joydeb Mondal; Pramir Sarkar
Venue: ACL 2025 (Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025))

Abstract: This paper presents CAViLR, a hybrid multimodal approach for SemEval-2025 Task 1. Our methodintegrates CLIP as a baseline with a Mixture of Experts (MoE) framework that dynamically selectsexpert models such as Pixtral-12B and Phi-3.5 based on input context. The approach addresseschallenges in both image ranking and image sequence prediction, improving the alignment of visualand textual semantics. Experimental results demonstrate that our hybrid model outperforms individualmodels. Future work will focus on refining expert selection and enhancing disambiguation strategiesfor complex idiomatic expressions.
ABCD at SemEval-2025 Task 9: BERT-based and Generation-based models combine with advanced weighted majority soft voting strategy
Authors: Le Duc Tai; Dang Van Thin
Venue: ACL 2025 (Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025))

Abstract: This paper illustrates our ABCD team system approach in ACL 2025 - SemEval-2025 Task 9: The Food Hazard Detection Challenge, aim to solving both Task 1: Text classification for food hazard prediction, predicting the type of hazard and product, and Task 2: Food hazard and product “vector” detection, predicting the exact hazard and product. Precisely, we received a food report and our system needed to automatically detect which category of hazard and product the food belonged to. However, in Task 2, we must classify the food report into the exact name of the food hazard and category. To tackle Task 1, we implement and investigate various solutions, including (1) experimenting with a large battery of BERT-based models; and (2) utilizing generation-based models, and (3) taking advantage of a custom ensemble learning method. In addition, to address Task 2, we make use of different data augmentation techniques like synonym replacement and back-translation. To enhance the context of input, we cleaned some special characters that bring more clarity into text input. Our best official results on Task 1 and Task 2 are 0.786 and 0.458 in terms of F1-score, respectively—finally, our team solution achieved top 8th in task 1 and top 10th in task 2.
Sakura at SemEval-2025 Task 2: Enhancing Named Entity Translation with Fine-Tuning and Preference Optimization
Authors: Alberto Poncelas; Ohnmar Htun
Venue: ACL 2025 (Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025))

Abstract: Translating name entities can be challenging, as it often requires real-world knowledge rather than just performing a literal translation. The shared task “Entity-Aware Machine Translation” in SemEval-2025 encourages participants to build machine translation models that can effectively handle the translation of complex named entities.In this paper, we propose two methods to improve the accuracy of name entity translation from English to Japanese. One approach involves fine-tuning the model on entries, or lists of entries, of the dictionary. The second technique focuses on preference optimization, guiding the model on which translation it should generate.
GOLDX at SemEval-2025 Task 11: RoBERTa for Text-Based Emotion Detection
Authors: Bill Clinton
Venue: ACL 2025 (Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025))

Abstract: This is an ensemble of RoBERTa models based approach to classify emotions from texts.
EMO-NLP at SemEval-2025 Task 11: Multi-label Emotion Detection in Multiple Languages Based on XLMCNN
Authors: Jing Li; Yucheng Xian; Xutao Yang
Venue: ACL 2025 (Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025))

Abstract: This paper describes the system implemented by the EMO-NLP team for track A of task 11 in SemEval-2025: Bridging the Gap in Text-Based Emotion Detection. The task focuses on multiple datasets covering 28 languages for multi-label emotion detection. Most of these languages are low-resource languages. To achieve this goal, we propose a multilingual multi-label emotion detection system called XLMCNN, which can perform multi-label emotion detection across multiple languages. To enable emotion detection in various languages, we utilize the pre-trained model XLM-RoberTa-large to obtain embeddings for the text in different languages. Subsequently, we apply a two-dimensional convolutional operation to the embeddings to extract text features, thereby enhancing the accuracy of multi-label emotion detection. Additionally, we assign weights to different emotion labels to mitigate the impact of uneven label distribution. In this task, we focus on nine languages, among which the Amharic language achieves the best performance with our system, ranking 21st out of 45 teams.
Anaselka at SemEval-2025 Task 9: Leveraging SVM and MNB for Detecting Food Hazard
Authors: Anwar Annas; Al Hafiz Siagian
Venue: ACL 2025 (Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025))

Abstract: Our system for the Sub-task 1 of SemEval-2025 Task 9 has been designed to tackle the complexities of identifying and categorizing food safety incidents from textual data. Through a rigorous experimental setup, we have developed a text classification solution that leveraged state-of-the-art techniques in data preprocessing, feature engineering, and model optimization.
MyMy at SemEval-2025 Task 9: A Robust Knowledge-Augmented Data Approach for Reliable Food Hazard Detection
Authors: Ben Phan; Jung-Hsien Chiang
Venue: ACL 2025 (Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025))

Abstract: The Food Hazard Detection (SemEval-2025 Task 9) advances explainable classification of food-incident reports collected from web sources, including social media and regulatory agency websites, to support timely risk mitigation for public health and the economy. This task is complicated by a highly imbalanced, long-tail label distribution and the need for transparent, reliable AI. We present a robust Knowledge-Augmented Data approach that integrates Retrieval-Augmented Generation (RAG) with domain-specific knowledge from the PubMed API to enrich and balance the training data. Our method leverages domain-specific knowledge to expand datasets and curate high-quality data that enhances overall data integrity. We hypothesize that Knowledge-Augmented Data improves Macro-F1 scores, the primary evaluation metric. Our approach achieved a top-2 ranking across both subtasks, demonstrating its effectiveness in advancing NLP applications for food safety and contributing to more reliable food hazard detection.