Multilingual Polarization Detection Using Transformer-Based Models with Class Weighting and Threshold Tuning

• URL: http://arxiv.org/abs/2606.30857v1
• Date: Mon, 29 Jun 2026 19:42:52 GMT
• Title: Multilingual Polarization Detection Using Transformer-Based Models with Class Weighting and Threshold Tuning
• Abstract summary: We address binary polarization detection, polarization type classification, and manifestation identification for English and Swahili.<n>Our approach leverages transformer-based models with class-weighted loss functions to address severe label imbalance.<n>Our error analysis reveals that models struggle with dehumanization detection and lack of empathy.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper describes our submission to SemEval-2026 Task 9 on detecting multilingual, multicultural, and multievent online polarization. We address all three subtasks: binary polarization detection, polarization type classification, and manifestation identification for English and Swahili. Our approach leverages transformer-based models (RoBERTa-base for English, AfroXLMR-base for Swahili) with class-weighted loss functions to address severe label imbalance and per-label threshold tuning to optimize multi-label classification. On the test set, we achieve F1 macro scores of 0.7901 (English) and 0.7910 (Swahili) for Subtask 1, 0.4615 (English) and 0.4808 (Swahili) for Subtask 2 and 0.4791 (English) and 0.5830 (Swahili) for Subtask 3, which give competitive performance on the leaderboard, demonstrating the effectiveness of our methods for handling imbalanced multi-label polarization detection. Our error analysis reveals that models struggle with dehumanization detection and lack of empathy.

Related papers

• Beyond the Name: Demographic Leakage in De-Identified Résumés and Evaluation Artifacts in LLM Bias Audits [4.736836688901646]
  We investigate whether eliminating language fields resolves leakage across nine open-weight models and 620 résumés.<n>We isolate unstructured prose across five ethnocultural conditions and three cue-salience tiers.<n>Target-group recovery averages 0.757 overall and saturates at 1.000 under high salience.
  arXiv  Detail & Related papers  (2026-09-15T01:48:44Z)
• Lingo_Research_Group at SemEval-2026 Task 9: Evaluating Prompt Variants for Polarization Detection [1.921560550802477]
  We present a submission for SemEval-2026 Task 9: Multilingual Text Classification Challenge - Polarization Detection.<n>It covers all three subtasks: (1) binary polarization detection, (2) polarization type classification and (3) polarization manifestation identification.<n>We adopt a systematic approach of research on short designed prompts by considering twelve designed prompts that are different in terminology clarity.
  arXiv  Detail & Related papers  (2026-06-02T08:41:54Z)
• A Modelling and Evaluation Framework for EuroCrops-Driven Sentinel-2 Crop Segmentation [78.66324246922831]
  This work presents a pipeline for generating semantic-segmentation-ready agricultural datasets from Sentinel-2 imagery and EuroCrops parcel-level annotations.<n>The dataset contains 67,337 patches from five European countries and uses a reduced taxonomy of ten crop classes plus background.<n>A four-level U-Net with Group Normalization was trained using 10 Sentinel-2 spectral bands and a composite loss combining class-weighted cross-entropy and Dice loss.
  arXiv  Detail & Related papers  (2026-05-30T11:20:29Z)
• YEZE at SemEval-2026 Task 9: Detecting Multilingual, Multicultural and Multievent Online Polarization via Heterogeneous Ensembling [3.3438708071539445]
  We propose a heterogeneous ensemble of multilingual pretrained models, combining XLM-RoBERTa-large and mDeBERTa-v3-base.<n>We investigate techniques such as multi-task learning, translation-based data augmentation, and class weighting to improve classification performance under severe label imbalance.
  arXiv  Detail & Related papers  (2026-05-07T13:21:40Z)
• Do We Still Need Humans in the Loop? Comparing Human and LLM Annotation in Active Learning for Hostility Detection [68.37351671559675]
  Active learning can annotate thousands of instances from a short prompt at negligible cost.<n>This raises two questions for active learning (AL): can LLM labels replace human labels within the AL loop, and does AL remain necessary when entire corpora can be labelled at once?<n>We investigate both questions on a new dataset of 277,902 German political TikTok comments.
  arXiv  Detail & Related papers  (2026-04-15T14:10:58Z)
• SemEval-2026 Task 9: Detecting Multilingual, Multicultural and Multievent Online Polarization [47.011443052131376]
  We present SemEval-2026 Task 9, a shared task on online polarization detection, covering 22 languages and comprising over 110K annotated instances.<n>Participants were asked to predict labels in three sub-tasks: (1) detecting the presence of polarization, (2) identifying the type of polarization, and (3) recognizing the polarization manifestation.<n>We report the baseline results and analyze the performance of the best-performing systems, highlighting the most common approaches and the most effective methods across different subtasks and languages.
  arXiv  Detail & Related papers  (2026-04-08T08:34:55Z)
• Not All Pretraining are Created Equal: Threshold Tuning and Class Weighting for Imbalanced Polarization Tasks in Low-Resource Settings [0.0]
  This paper describes my submission to the Polarization Shared Task at SemEval-2025, which addresses polarization detection and classification in social media text.<n>I develop Transformer-based systems for English and Swahili across three subtasks: binary polarization detection, multi-label target type classification, and multi-label manifestation identification.
  arXiv  Detail & Related papers  (2026-03-08T15:50:37Z)
• Mario at EXIST 2025: A Simple Gateway to Effective Multilingual Sexism Detection [8.40042895828361]
  EXIST 2025 Task 1 addresses text-based sexism detection in English and Spanish tweets through hierarchical Low-Rank Adaptation (LoRA) of Llama 3.1 8B.<n>Our method introduces conditional adapter routing that explicitly models dependencies across three hierarchically structured subtasks.<n>Our approach reduces training time by 75% and model storage by 98%, while achieving competitive performance across all subtasks.
  arXiv  Detail & Related papers  (2025-07-15T05:30:32Z)
• Bag of Tricks for Effective Language Model Pretraining and Downstream Adaptation: A Case Study on GLUE [93.98660272309974]
  This report briefly describes our submission Vega v1 on the General Language Understanding Evaluation leaderboard. GLUE is a collection of nine natural language understanding tasks, including question answering, linguistic acceptability, sentiment analysis, text similarity, paraphrase detection, and natural language inference. With our optimized pretraining and fine-tuning strategies, our 1.3 billion model sets new state-of-the-art on 4/9 tasks, achieving the best average score of 91.3.
  arXiv  Detail & Related papers  (2023-02-18T09:26:35Z)
• OneAligner: Zero-shot Cross-lingual Transfer with One Rich-Resource Language Pair for Low-Resource Sentence Retrieval [91.76575626229824]
  We present OneAligner, an alignment model specially designed for sentence retrieval tasks. When trained with all language pairs of a large-scale parallel multilingual corpus (OPUS-100), this model achieves the state-of-the-art result. We conclude through empirical results and analyses that the performance of the sentence alignment task depends mostly on the monolingual and parallel data size.
  arXiv  Detail & Related papers  (2022-05-17T19:52:42Z)
• DeBERTaV3: Improving DeBERTa using ELECTRA-Style Pre-Training with Gradient-Disentangled Embedding Sharing [117.41016786835452]
  This paper presents a new pre-trained language model, DeBERTaV3, which improves the original DeBERTa model. vanilla embedding sharing in ELECTRA hurts training efficiency and model performance. We propose a new gradient-disentangled embedding sharing method that avoids the tug-of-war dynamics.
  arXiv  Detail & Related papers  (2021-11-18T06:48:00Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.