KIT-TIP-NLP at MultiPride: Continual Learning with Multilingual Foundation Model

• URL: http://arxiv.org/abs/2605.13415v2
• Date: Mon, 18 May 2026 00:36:02 GMT
• Title: KIT-TIP-NLP at MultiPride: Continual Learning with Multilingual Foundation Model
• Abstract summary: The framework handles three intertwined methodological challenges like data scarcity, class imbalance, and cross-linguistic variation in sentiment expression.<n>It integrates data-driven model selection via cross-validation, semantic-preserving augmentation through back-translation, inductive transfer learning with dynamic epoch-level undersampling, and domain-specific knowledge injection.<n>It produces four final runs for the evaluation purposes where RUN 1 is inductive transfer learning with augmentation and undersampling, RUN 2 with masked language modeling pre-training, RUN 3 and RUN 4 are previous predictions refined via language-specific decision thresholds optimized via ROC analysis.
• Score: 0.06999740786886534
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper presents a multi-stage framework for detecting reclaimed slurs in multilingual social media discourse. It addresses the challenge of identifying reclamatory versus non-reclamatory usage of LGBTQ+-related slurs across English, Spanish, and Italian tweets. The framework handles three intertwined methodological challenges like data scarcity, class imbalance, and cross-linguistic variation in sentiment expression. It integrates data-driven model selection via cross-validation, semantic-preserving augmentation through back-translation, inductive transfer learning with dynamic epoch-level undersampling, and domain-specific knowledge injection via masked language modeling. Eight multilingual embedding models were evaluated systematically, with XLM-RoBERTa selected as the foundation model based on macro-averaged F1 score. Data augmentation via GPT-4o-mini back-translation to alternate languages effectively tripled the training corpus while preserving semantic content and class distribution ratios. The framework produces four final runs for the evaluation purposes where RUN 1 is inductive transfer learning with augmentation and undersampling, RUN 2 with masked language modeling pre-training, RUN 3 and RUN 4 are previous predictions refined via language-specific decision thresholds optimized via ROC analysis. Language-specific threshold refinement reveals that optimal decision boundaries vary significantly across languages. This reflects distributional differences in model confidence scores and linguistic variation in reclamatory language usage. The threshold-based optimization yields 2-5% absolute F1 improvement without requiring model retraining. The methodology is fully reproducible, with all code and experimental setup available at https://github.com/rbg-research/MultiPRIDE-Evalita-2026.

Related papers

• COMPASS: COntinual Multilingual PEFT with Adaptive Semantic Sampling [0.023074632109535153]
  We introduce a data-centric framework for adapting large language models to target languages.<n>We use a distribution-aware sampling strategy to identify semantic gaps between existing training data and a target usage distribution.<n>We extend this into a continual learning framework, which monitors for data distribution shifts in production and dynamically updates adapters to prevent model staleness.
  arXiv  Detail & Related papers  (2026-04-22T16:07:10Z)
• Cross-Language Speaker Attribute Prediction Using MIL and RL [0.48998185508205744]
  We study multilingual speaker attribute prediction under linguistic variation, domain mismatch, and data imbalance across languages.<n>We propose RLMIL-DAT, a multilingual extension of the reinforced multiple instance learning framework.<n>We evaluate the approach on a five language Twitter corpus in a few shot setting and on a VoxCeleb2 derived corpus covering forty languages in a zero shot setting for gender and age prediction.
  arXiv  Detail & Related papers  (2026-01-06T23:07:26Z)
• The Unreasonable Effectiveness of Model Merging for Cross-Lingual Transfer in LLMs [45.08958917457921]
  Large language models (LLMs) still struggle across tasks outside of high-resource languages.<n>In this work, we investigate cross-lingual transfer to lower-resource languages where task-specific post-training data is scarce.
  arXiv  Detail & Related papers  (2025-05-23T20:28:31Z)
• A New Method for Cross-Lingual-based Semantic Role Labeling [5.992526851963307]
  A deep learning algorithm is proposed to train semantic role labeling in English and Persian. The results show significant improvements compared to Niksirt et al.'s model. The development of cross-lingual methods for semantic role labeling holds promise.
  arXiv  Detail & Related papers  (2024-08-28T16:06:12Z)
• Multilingual and Multi-topical Benchmark of Fine-tuned Language models and Large Language Models for Check-Worthy Claim Detection [1.4779899760345434]
  This study compares the performance of (1) fine-tuned language models and (2) large language models on the task of check-worthy claim detection. We composed a multilingual and multi-topical dataset comprising texts of various sources and styles.
  arXiv  Detail & Related papers  (2023-11-10T15:36:35Z)
• T3L: Translate-and-Test Transfer Learning for Cross-Lingual Text Classification [50.675552118811]
  Cross-lingual text classification is typically built on large-scale, multilingual language models (LMs) pretrained on a variety of languages of interest. We propose revisiting the classic "translate-and-test" pipeline to neatly separate the translation and classification stages.
  arXiv  Detail & Related papers  (2023-06-08T07:33:22Z)
• Improving the Cross-Lingual Generalisation in Visual Question Answering [40.86774711775718]
  multilingual vision-language pretrained models show poor cross-lingual generalisation when applied to non-English data. In this work, we explore the poor performance of these models on a zero-shot cross-lingual visual question answering (VQA) task. We improve cross-lingual transfer with three strategies: (1) we introduce a linguistic prior objective to augment the cross-entropy loss with a similarity-based loss to guide the model during training, (2) we learn a task-specific subnetwork that improves cross-lingual generalisation and reduces variance without model modification, and (3) we augment training examples using synthetic code
  arXiv  Detail & Related papers  (2022-09-07T08:07:43Z)
• A Multi-level Supervised Contrastive Learning Framework for Low-Resource Natural Language Inference [54.678516076366506]
  Natural Language Inference (NLI) is a growingly essential task in natural language understanding. Here we propose a multi-level supervised contrastive learning framework named MultiSCL for low-resource natural language inference.
  arXiv  Detail & Related papers  (2022-05-31T05:54:18Z)
• From Good to Best: Two-Stage Training for Cross-lingual Machine Reading Comprehension [51.953428342923885]
  We develop a two-stage approach to enhance the model performance. The first stage targets at recall: we design a hard-learning (HL) algorithm to maximize the likelihood that the top-k predictions contain the accurate answer. The second stage focuses on precision: an answer-aware contrastive learning mechanism is developed to learn the fine difference between the accurate answer and other candidates.
  arXiv  Detail & Related papers  (2021-12-09T07:31:15Z)
• Distributionally Robust Multilingual Machine Translation [94.51866646879337]
  We propose a new learning objective for Multilingual neural machine translation (MNMT) based on distributionally robust optimization. We show how to practically optimize this objective for large translation corpora using an iterated best response scheme. Our method consistently outperforms strong baseline methods in terms of average and per-language performance under both many-to-one and one-to-many translation settings.
  arXiv  Detail & Related papers  (2021-09-09T03:48:35Z)
• Boosting Cross-Lingual Transfer via Self-Learning with Uncertainty Estimation [34.97086123805344]
  Recent multilingual pre-trained language models have achieved remarkable zero-shot performance. We propose a self-learning framework that further utilizes unlabeled data of target languages. We evaluate our framework with uncertainties on two cross-lingual tasks including Named Entity Recognition (NER) and Natural Language Inference (NLI) covering 40 languages in total.
  arXiv  Detail & Related papers  (2021-09-01T05:26:46Z)
• Unsupervised Domain Adaptation of a Pretrained Cross-Lingual Language Model [58.27176041092891]
  Recent research indicates that pretraining cross-lingual language models on large-scale unlabeled texts yields significant performance improvements. We propose a novel unsupervised feature decomposition method that can automatically extract domain-specific features from the entangled pretrained cross-lingual representations. Our proposed model leverages mutual information estimation to decompose the representations computed by a cross-lingual model into domain-invariant and domain-specific parts.
  arXiv  Detail & Related papers  (2020-11-23T16:00:42Z)
• Cross-lingual Spoken Language Understanding with Regularized Representation Alignment [71.53159402053392]
  We propose a regularization approach to align word-level and sentence-level representations across languages without any external resource. Experiments on the cross-lingual spoken language understanding task show that our model outperforms current state-of-the-art methods in both few-shot and zero-shot scenarios.
  arXiv  Detail & Related papers  (2020-09-30T08:56:53Z)
• XCOPA: A Multilingual Dataset for Causal Commonsense Reasoning [68.57658225995966]
  Cross-lingual Choice of Plausible Alternatives (XCOPA) is a typologically diverse multilingual dataset for causal commonsense reasoning in 11 languages. We evaluate a range of state-of-the-art models on this novel dataset, revealing that the performance of current methods falls short compared to translation-based transfer.
  arXiv  Detail & Related papers  (2020-05-01T12:22:33Z)

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.