Hybrid Multilingual Transformer and Linguistic Feature Fusion for Apology Sincerity Detection

LLM-based Multilingual Fake News Detection using Cross-Lingual Semantic Alignment and Contextual Reasoning

The production of misinformation on digital platforms has been a concern in recent years, especially in interlingual contexts – people who create content in different languages and language elements that are integrated into written content.the existing fake news detectors use mostly only one language, and they are not adequately cross-linguistically generalizable in using the valuable detectors especially with low-resource languages. To overcome these weaknesses, the present paper suggests an LLM-based multilingual fake news detector which combines cross-lingual semantic alignment and contextual reasoning. The proposed approach involves the fine-tuned multilingual language model, which involves a transformer to derive contextual embeddings of multilingual text. The semantic alignment process (contrastive) is used to project the representation of the various languages to a common embedding space, and hence the transfer of knowledge. Moreover, semantic inconsistencies and misguiding tendency contribute to the reasoning of the model deducing abilities applied in the presence of attention processes. Experiments with the mixed English, Hindi, and Tamil data reveal that the proposed model leads to the accuracy of 94.3 and the F1-score of 93.6, which is better than a baseline model, including SVM, LSTM, and mBERT. The results enable mentioning the great overallization possibility and low-resource, multilingual applicability of the model. The paper gives a scaled and efficient approach to tackling the real-life aspect of multilingual fake news detection.

A Systematic Ablation Study of Multilingual Transformer Architectures for Sarcasm Detection: Architectural Design Versus Feature Engineering

Sarcasm detection remains a challenging task in natural language processing due to the complex interaction between linguistic and pragmatic cues. While transformer-based models have shown encouraging performance, it is not yet clear whether improvements stem primarily from architectural design or explicit feature engineering. This work introduces a systematic ablation study of three multilingual transformer architectures (mBERT, mDeBERTa-v3, and XLM-RoBERTa) evaluated on HeteroSarc-47K, a curated multi-domain dataset containing 47,694 instances across six heterogeneous domains, including social media and dialectal Arabic text. We isolate the empirical impact of two feature augmentation techniques: emoji integration and contrastive pre-training. Our experiments reveal that while contrastive pre-training increases sarcasm recall for mBERT, it induces a significant drop in its overall accuracy, demonstrating architecture-specific sensitivities. In contrast, architectural upgrades alone consistently yield robust improvements; mDeBERTa-v3 achieves the highest performance (72.79% sarcasm recall and 87.20% accuracy) without requiring any feature augmentations, outperforming the baseline mBERT. Furthermore, per-class analysis reveals a persistent performance gap between non-sarcastic and sarcastic classes across all domains, highlighting that class imbalance remains a critical challenge independent of architecture. Ultimately, these findings provide a vital practical takeaway for practitioners: upgrading to inherently stronger multilingual architectures yields significantly higher and more robust performance returns compared to investing in extensive feature-engineering pipelines.

Lamba: Mamba State Space Model Adapted to Latin Semantic Retrieval ⋆

Temporal and linguistic enrichment for abstractive text summarization using transformer models

Abstractive text summarization is a critical yet challenging task in natural language processing, requiring models to generate concise, coherent, and factually accurate summaries. Existing transformer-based architectures, such as BART and T5, often underperform in scenarios where language semantics evolve over time or when lexical diversity is limited. These limitations result in summaries that lack contextual relevance and semantic generalization, especially in dynamic domains such as news reporting. In this work, we present a lightweight hybrid abstractive summarization model that enhances temporal awareness and linguistic flexibility. The model integrates synonym-based augmentation and time-sensitive embeddings into a base transformer encoder-decoder architecture, enabling the generation of temporally aligned and semantically enriched summaries. To further improve reliability, we introduce a factual alignment evaluation module that assesses entity-level consistency using named entity recognition. We conduct extensive experiments on two benchmark datasets, XSum and CNN/Daily Mail, and evaluate the model using a wide range of metrics, including ROUGE, BLEU, METEOR, BERTScore, and factual consistency checks. The ablation study confirms the individual contributions of temporal modeling and synonym enrichment, with the combined model achieving up to 8.3% ROUGE-1 and 7.9% METEOR improvements over the baseline. The model demonstrates practical applicability for summarization systems requiring both temporal relevance and semantic robustness, while maintaining CPU efficiency and interpretability. Implications for downstream tasks like news trend analysis are also explored.

Leveraging LLMs to Automatically Construct WordNets as Bilingual Resources

Semantic-Symbolic Knowledge Consensus for Multilingual Question Answering

This paper proposes SeSyCo, a Semantic-Symbolic Knowledge Consensus framework, which leverages the semantic space to diverge monolingual queries into broad multilingual evidence, and subsequently utilize the symbolic space to eliminate language discrepancies, converging the gathered information into a robust consensus for precise SPARQL generation.