GenAI4ELab/papercli-papers huggingface dataset:
Team A at SemEval-2025 Task 11: Breaking Language Barriers in Emotion Detection with Multilingual Models
Authors: P Sam Sahil; Anupam Jamatia
Venue: ACL 2025 (Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025))

Abstract: This paper describes the system submitted by Team A to SemEval 2025 Task 11, “Bridging the Gap in Text-Based Emotion Detection.” The task involved identifying the perceived emotion of a speaker from text snippets, with each instance annotated with one of six emotions: joy, sadness, fear, anger, surprise, or disgust. A dataset provided by the task organizers served as the foundation for training and evaluating our models. Among the various approaches explored, the best performance was achieved using multilingual embeddings combined with a fully connected layer. This paper details the system architecture, discusses experimental results, and highlights the advantages of leveraging multilingual representations for robust emotion detection in text.
YNU-HPCC at SemEval-2025 Task 11: Bridging the Gap in Text-Based Emotion Using Multiple Prediction Headers
Authors: Hao Yang; Jin Wang; Xuejie Zhang
Venue: ACL 2025 (Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025))

Abstract: This paper describes the our team’s participation in Subtask A of Task 11 at SemEval-2025, focusing on multilingual text-based emotion classification. The team employed the RoBERTa model, enhanced with modifications to the output head to allow independent prediction of six emotions: anger, disgust, fear, joy, sadness, and surprise. The dataset was translated into English using Google Translate to facilitate processing. The study found that a single prediction head outperformed simultaneous prediction of multiple emotions, and training on the translated dataset yielded better results than using the original dataset. The team incorporated Focal Loss and R-Drop techniques to address class imbalance and improve model stability. Future work will continue to explore improvements in this area.
I2R-NLP at SemEval-2025 Task 8: Question Answering on Tabular Data
Authors: Yuze Gao; Bin Chen; Jian Su
Venue: ACL 2025 (Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025))

Abstract: We present a Large Language Model (LLM) based system for question answering (QA) over tabular data that leverages multi-turn prompting to automatically generate executable Pandas functions. Our framework decomposes the problem into three key steps: (1) Answer Type Identification, where the system identifies the expected format of the response (e.g., boolean, number, category); (2) Pandas Function Generation, which generates a corresponding Pandas function using table metadata and in-context examples, and (3) Error Correction and Regeneration, where iteratively refining the function based on error feedback from executions. Evaluations on the SemEval-2025 Task 8 Tabular QA benchmark (Grijalba et al., 2024) demonstrate that our multi-turn approach significantly outperforms single-turn prompting models in exact match accuracy by 7.3%. The proposed system not only improves code generation robustness but also paves the way for enhanced and adaptability in table-QA reasoning tasks. Our implementation is available at https://github.com/Gyyz/Question_Answering-over-Tabular-Data.
TeleAI at SemEval-2025 Task 11: Bridging the Gap in Text-Based Emotion Detection with Prompt Engineering and Data Augmentation
Authors: Shiquan Wang; Mengxiang Li; Shengxiong Peng; Fang Yu; Zhongjiang He; Shuangyong Song; Yongxiang Li
Venue: ACL 2025 (Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025))

Abstract: This paper presents the approach we employed in SemEval-2025 Task 11: “Bridging the Gap in Text-Based Emotion Detection.” The core objective of this shared task is emotion perception, focusing on determining the emotion the speaker is likely expressing when uttering a sentence or short text fragment, as perceived by the majority. In this task, we applied a prompt optimization strategy based on in-context learning, combined with data augmentation and ensemble voting techniques, to significantly enhance the model’s performance. Through these optimizations, the model demonstrated improved accuracy and stability in emotion detection. Ultimately, in both Track A (Multi-label Emotion Detection) and Track B (Emotion Intensity Prediction), our approach achieved top-3 rankings across multiple languages, showcasing the effectiveness and cross-lingual adaptability of our method.
OZemi at SemEval-2025 Task 11: Multilingual Emotion Detection and Intensity
Authors: Hidetsune Takahashi; Sumiko Teng; Jina Lee; Wenxiao Hu; Rio Obe; Chuen Shin Yong; Emily Ohman
Venue: ACL 2025 (Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025))

Abstract: This paper presents the OZemi team’s submission to SemEval-2025 Task 11: Multilingual Emotion Detection and Intensity. Our approach prioritized computational efficiency, leveraging lightweight models that achieved competitive results even for low-resource languages. We addressed data imbalance through data augmentation techniques such as back translation and class balancing. Our system utilized multilingual BERT and machine translation to enhance performance across 35 languages. Despite ranking mid-tier overall, our results demonstrate that relatively simple models can yield adequate performance across diverse linguistic settings. We provide an error analysis of emotion classification challenges, particularly for nuanced expressions such as sarcasm and irony, and discuss the impact of emoji representation on model predictions. Finally, we outline future directions, including improvements in sentiment intensity modeling and the integration of semantic prosody to refine emotion detection.
DUT_IR at SemEval-2025 Task 11: Enhancing Multi-Label Emotion Classification with an Ensemble of Pre-trained Language Models and Large Language Models
Authors: Chao Liu; Junliang Liu; Tengxiao Lv; Huayang Li; Tao Zeng; Ling Luo; Yuanyuan Sun; Hongfei Lin
Venue: ACL 2025 (Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025))

Abstract: In this work, we tackle the challenge of multi-label emotion classification, where a sentence can simultaneously express multiple emotions. This task is particularly difficult due to the overlapping nature of emotions and the limited context available in short texts. To address these challenges, we propose an ensemble approach that integrates Pre-trained Language Models (BERT-based models) and Large Language Models, each capturing distinct emotional cues within the text. The predictions from these models are aggregated through a voting mechanism, enhancing classification accuracy. Additionally, we incorporate threshold optimization and class weighting techniques to mitigate class imbalance. Our method demonstrates substantial improvements over baseline models. Our approach ranked 4th out of 90 on the English leaderboard and exhibited strong performance in English in SemEval-2025 Task 11 Track A.
ChuenSumi at SemEval-2025 Task 1: Sentence Transformer Models and Processing Idiomacity
Authors: Sumiko Teng; Chuen Shin Yong
Venue: ACL 2025 (Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025))

Abstract: This paper participates Task 1 of SemEval2025, specifically Subtask A’s English Text-Only track, where we develop a model to rank text descriptions of images with respect to how well it represents a the use of a given multi-word expression in its respective context sentence. We trained sentence transformer models from huggingface to rank the text descriptions, finding the RoBERTa model to be the better performing model. For the final evaluation, the fine-tuned RoBERTa model achieved an accuracy of 0.4 for the first developer’s evaluation set, and 0.2 for the second, ranking 9th in the English Text Only category for Subtask A. Overall, our results show that a vanilla sentence transformerapproach performs adequately in the task and processing idioms. They also suggest that RoBERTa models may be stronger in idiom processing than other models.
daalft at SemEval-2025 Task 1: Multi-step Zero-shot Multimodal Idiomaticity Ranking
Authors: David Alfter
Venue: ACL 2025 (Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025))

Abstract: This paper presents a multi-step zero-shot system for SemEval-2025 Task 1 on Advancing Multimodal Idiomaticity Representation (AdMIRe). The system employs two state-of-the-art multimodal language models, Claude Sonnet 3.5 and OpenAI GPT-4o, to determine idiomaticity and rank images for relevance in both subtasks. A hybrid approach combining o1-preview for idiomaticity classification and GPT-4o for visual ranking produced the best overall results. The system demonstrates competitive performance on the English extended dataset for Subtask A, but faces challenges in cross-lingual transfer to Portuguese. Comparing Image+Text and Text-Only approaches reveals interesting trends and raises questions about the role of visual information in multimodal idiomaticity detection.
Anastasia at SemEval-2025 Task 9: Subtask 1, Ensemble Learning with Data Augmentation and Focal Loss for Food Risk Classification.
Authors: Tung Thanh Le; Tri Minh Ngo; Trung Hieu Dang
Venue: ACL 2025 (Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025))

Abstract: Our approach for the SemEval-2025 Task 9: Subtask 1, The Food Hazard Detection Challenge showcases a robust ensemble learning methodology designed to classify food hazards and associated products from incident report titles. By incorporating advanced data augmentation techniques, we significantly enhanced model generalization and addressed class imbalance through the application of focal loss. This strategic combination led to our team securing the Top 1 position, achieving an impressive score of 0.8223, underscoring the strength of our solution in improving classification performance for food safety risk assessment.
GateNLP at SemEval-2025 Task 10: Hierarchical Three-Step Prompting for Multilingual Narrative Classification
Authors: Iknoor Singh; Carolina Scarton; Kalina Bontcheva
Venue: ACL 2025 (Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025))

Abstract: The proliferation of online news and the increasing spread of misinformation necessitate robust methods for automated narrative classification. This paper presents our approach to SemEval 2025 Task 10 Subtask 2, which aims to classify news articles into a predefined two-level taxonomy of main narratives and sub-narratives across multiple languages. We propose Hierarchical Three-Step Prompting (H3Prompt) for multilingual narrative classification. Our methodology follows a three-step prompting strategy, where the model first categorises an article into one of two domains (Ukraine-Russia War or Climate Change), then identifies the most relevant main narratives, and finally assigns sub-narratives. Our approach secured the top position on the English test set among 28 competing teams worldwide. This result highlights the effectiveness of our method in improving narrative classification performance over the baselines.
DKE-Research at SemEval-2025 Task 7: A Unified Multilingual Framework for Cross-Lingual and Monolingual Retrieval with Efficient Language-specific Adaptation
Authors: Yuqi Wang; Kangshi Wang
Venue: ACL 2025 (Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025))

Abstract: This paper presents a unified framework for fact-checked claim retrieval, integrating contrastive learning with an in-batch multiple negative ranking loss and a conflict-aware batch sampler to enhance query-document alignment across languages. Additionally, we introduce language-specific adapters for efficient fine-tuning, enabling adaptation to previously unseen languages.
wangkongqiang at SemEval-2025 Task 11:Bridging the Gap in Text-Based Emotion Detection
Authors: Wang Kongqiang
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 11:Bridging the Gap in Text-Based Emotion Detection, on Track A: Multi-label Emotion Detection.Given a target text snippet, predict the perceived emotion(s) of the speaker. Specifically, select whether each of the following emotions apply: joy, sadness, fear, anger, surprise, or disgust. To this end, we focus on English source language selection strategies on four different pre-trained languages models: google-bert,FacebookAI-roberta,dccuchile-bert and distilbert-multi.We experiment with 1) the training set data is analyzed visually, 2) multiple numbers of single models are trained on the training set data, and 3) multiple number of single models for votingweight ensemble learning. We further study the influence of different hyperparameters on the integrated model and select the best integration model for the prediction of the test set. Our submission achieved the good ranking place in the test set.Emotion Macro F1 Score 0.6998 and Emotion Micro F1 Score 0.7374. For the final ranking, organizers will use the Macro F1 score.Even so, my approach has yielded good results.
UNEDTeam at SemEval-2025 Task 10: Zero-Shot Narrative Classification
Authors: Jesus M. Fraile - Hernandez; Anselmo Peñas
Venue: ACL 2025 (Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025))

Abstract: In this paper we present our participation in Subtask 2 of SemEval-2025 Task 10, focusing on the identification and classification of narratives in news of multiple languages, on climate change and the Ukraine-Russia war. To address this task, we employed a Zero-Shot approach using a generative Large Language Model without prior training on the dataset. Our classification strategy is based on two steps: first, the system classifies the topic of each news item; subsequently, it identifies the sub-narratives directly at the finer granularity. We present a detailed analysis of the performance of our system compared to the best ranked systems on the leaderboard, highlighting the strengths and limitations of our approach.
DUTIR at SemEval-2025 Task 10: A Large Language Model-based Approach for Entity Framing in Online News
Authors: Tengxiao Lv; Juntao Li; Chao Liu; Yiyang Kang; Ling Luo; Yuanyuan Sun; Hongfei Lin
Venue: ACL 2025 (Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025))

Abstract: We propose a multilingual text processing framework that combines multilingual translation with data augmentation, QLoRA-based multi-model fine-tuning, and GLM-4-Plus-based ensemble classification. By using GLM-4-Plus to translate multilingual texts into English, we enhance data diversity and quantity. Data augmentation effectively improves the model’s performance on imbalanced datasets. QLoRA fine-tuning optimizes the model and reduces classification loss. GLM-4-Plus, as a meta-classifier, further enhances system performance. Our system achieved first place in three languages (English, Portuguese and Russian).
PuerAI at SemEval-2025 Task 9: Research on Food Safety Data Classification Using ModernBERT
Authors: Jiaxu Dao; Zhuoying Li; Xiuzhong Tang; Youbang Su; Qingsong Zhou; Weida He; Xiaoli Lan
Venue: ACL 2025 (Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025))

Abstract: This paper presents our research in the SemEval-2025 Task 9: Food Hazard Detection Challenge, with a focus on the application of ModernBERT for food safety data classification. We applied the ModernBERT model for the food hazard classification task, achieving a score of 0.7952 on the validation set and 0.7729 on the final test set, outperforming other models. Through comparative experiments with various deep learning architectures, we further confirmed the superiority of ModernBERT in food hazard detection. The results demonstrate the significant potential of ModernBERT in food safety management, providing strong support for its practical applications in the field. The code of this paper is available at: https://github.com/daojiaxu/semeval_2025_Task-9.
TechSSN3 at SemEval-2025 Task 11: Multi-Label Emotion Detection Using Ensemble Transformer Models and Lexical Rules
Authors: Vishal S; Rajalakshmi Sivanaiah; Angel Deborah S
Venue: ACL 2025 (Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025))

Abstract: Transformer models, specifically BERT-Large Uncased, DeBERTa, and RoBERTa, are first employed to classify the dataset, with their hyperparameters being fine-tuned to identify the most effective configuration. These models leverage deep contextual embeddings to capture nuanced semantic and syntactic information, making them powerful for sentiment analysis. However, transformer-based models alone may not fully capture the structural aspects of sentiment-bearing sentences.To address this, part-of-speech (POS) tagging is incorporated using a Hidden Markov Model (HMM) to analyze sentence structure and identify the key words responsible for conveying sentiment. By isolating adjectives, adverbs, and verbs, the lexical sentiment of individual words is determined using a polarity-based scoring method. This lexical score, derived from sentiment lexicons like SentiWordNet, provides an additional layer of interpretability, particularly in cases where transformer models struggle with implicit sentiment cues or negation handling.A key innovation in this approach is the adaptive weighting mechanism used to combine the outputs of the transformer models and lexical scoring. Instead of assigning uniform importance to each method, a unique weight is assigned to each model for every emotion category, ensuring that the best-performing approach contributes more significantly to the final sentiment prediction. For instance, DeBERTa, which excels in contextual understanding, is given more weight for subtle emotions like sadness, whereas lexical scoring is emphasized for emotions heavily influenced by explicit adjectives, such as joy or anger. The weight allocation is determined empirically through performance evaluation on a validation set, ensuring an optimal balance between deep learning-based contextual understanding and rule-based sentiment assessment.Additionally, traditional machine learning models such as Support Vector Machines (SVMs), Decision Trees, and Random Forests are tested for comparative analysis. However, these models demonstrate inferior performance, struggling with capturing deep contextual semantics and handling nuanced expressions of sentiment, reinforcing the superiority of the hybrid transformer + lexical approach.This method not only enhances interpretability but also improves accuracy, particularly in cases where sentiment is influenced by structural elements, negations, or compound expressions. The combined framework ensures a more robust and adaptable sentiment analysis model, effectively balancing data-driven learning and linguistic insights.
iai_MSU at SemEval-2025 Task-3: Mu-SHROOM, the Multilingual Shared-task on Hallucinations and Related Observable Overgeneration Mistakes in English
Authors: Mikhail Pukemo; Aleksandr Levykin; Dmitrii Melikhov; Gleb Skiba; Roman Ischenko; Konstantin Vorontsov
Venue: ACL 2025 (Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025))

Abstract: This paper presents the submissions of the iai_MSU team for SemEval-2025 Task 3 – Mu-SHROOM, where we achieved first place in the English language. The task involves detecting hallucinations in model-generated text, which requires systems to verify claims against reliable sources.In this paper, we present our approach to hallucination detection, which employs a three-stage system. The first stage uses a retrieval-based (Lewis et al., 2021) to verify claims against external knowledge sources. The second stage applies the Self-Refine Prompting (Madaan et al., 2023) to improve detection accuracy by analyzing potential errors of the first stage. The third stage combines predictions from the first and second stages into an ensemble.Our system achieves state-of-the-art performance on the competition dataset, demonstrating the effectiveness of combining retrieval-augmented verification with Self-Refine Prompting. The code for the solutions is available on https://github.com/pansershrek/IAI_MSU.
CIOL at SemEval-2025 Task 11: Multilingual Pre-trained Model Fusion for Text-based Emotion Recognition
Authors: Md. Hoque; Mahfuz Ahmed Anik; Abdur Rahman; Azmine Toushik Wasi
Venue: ACL 2025 (Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025))

Abstract: Multilingual emotion detection is a critical challenge in natural language processing, enabling applications in sentiment analysis, mental health monitoring, and user engagement. However, existing models struggle with overlapping emotions, intensity quantification, and cross-lingual adaptation, particularly in low-resource languages. This study addresses these challenges as part of SemEval-2025 Task 11 by leveraging language-specific transformer models for multi-label classification (Track A), intensity prediction (Track B), and cross-lingual generalization (Track C). Our models achieved strong performance in Russian (Track A: 0.848 F1, Track B: 0.8594 F1) due to emotion-rich pretraining, while Chinese (0.483 F1) and Spanish (0.6848 F1) struggled with intensity estimation. Track C faced significant cross-lingual adaptation issues, with Russian (0.3102 F1), Chinese (0.2992 F1), and Indian (0.2613 F1) highlighting challenges in low-resource settings. Despite these limitations, our findings provide valuable insights into multilingual emotion detection. Future work should enhance cross-lingual representations, address data scarcity, and integrate multimodal information for improved generalization and real-world applicability.
UWBa at SemEval-2025 Task 7: Multilingual and Crosslingual Fact-Checked Claim Retrieval
Authors: Ladislav Lenc; Daniel Cífka; Jiri Martinek; Jakub Šmíd; Pavel Kral
Venue: ACL 2025 (Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025))

Abstract: This paper presents a zero-shot system for fact-checked claim retrieval. We employed several state-of-the-art large language models to obtain text embeddings. The models were then combined to obtain the best possible result. Our approach achieved 7th place in monolingual and 9th in cross-lingual subtasks. We used only English translations as an input to the text embedding models since multilingual models did not achieve satisfactory results. We identified the most relevant claims for each post by leveraging the embeddings and measuring cosine similarity. Overall, the best results were obtained by the NVIDIA NV-Embed-v2 model. For some languages, we benefited from model combinations (NV-Embed & GPT or Mistral).