The Iranic language family includes many underrepresented languages and dialects that remain largely unexplored in modern NLP research. We introduce APARSIN, a multi-variety benchmark covering 14 Iranic languages, dialects, and accents, designed for sentiment analysis and machine translation. The dataset includes both high and low-resource varieties, several of which are endangered, capturing linguistic variation across them. We evaluate a set of instruction-tuned Large Language Models (LLMs) on these tasks and analyze their performance across the varieties. Our results highlight substantial performance gaps between standard Persian and other Iranic languages and dialects, demonstrating the need for more inclusive multilingual and dialectally diverse NLP benchmarks.

Large language models (LLMs) have demonstrated strong performance on emotion understanding tasks, yet their ability to faithfully generate emotionally aligned text remains less well understood.We propose a semantic evaluation framework that jointly assesses emotion understanding, emotion generation, and internal consistency, using a VAE-based emotion cost matrix that captures graded semantic similarity between emotion categories.Our framework introduces four complementary metrics that disentangle baseline understanding, human-perceived emotion in generated text, generation quality, and model consistency.Experimental results show that while understanding and consistency scores are highly correlated, emotion generation exhibits substantially weaker correlations with these metrics.These findings motivate the development of specialized evaluation protocols that independently measure emotional understanding and generation, enabling more reliable assessments of LLM emotional intelligence.

DadmaTools V2 is a comprehensive repository designed to enhance NLP capabilities for the Persian language, catering to industry practitioners seeking practical and efficient solutions. The toolkit provides extensive code examples demonstrating the integration of its models with popular NLP frameworks such as Trankit and Transformers, as well as deep learning frameworks like PyTorch. Additionally, DadmaTools supports widely used Persian embeddings and datasets, ensuring robust language processing capabilities. The latest version of DadmaTools introduces an adapter-based technique, significantly reducing memory usage by employing a shared pre-trained model across various tasks, supplemented with task-specific adapter layers. This approach eliminates the need to maintain multiple pre-trained models and optimize resource utilization. Enhancements in this version include adding new modules such as a sentiment detector, an informal-to-formal text converter, and a spell checker, further expanding the toolkit’s functionality. DadmaTools V2 thus represents a powerful, efficient, and versatile resource for advancing Persian NLP applications.

Mental health disorders such as stress, anxiety, and depression are increasingly prevalent globally, yet access to care remains limited due to barriers like geographic isolation, financial constraints, and stigma. Conversational agents or chatbots have emerged as viable digital tools for personalized mental health support. This paper presents the development of a psychological health chatbot designed specifically for Persian-speaking individuals, offering a culturally sensitive tool for emotion detection and disorder identification. The chatbot integrates several advanced natural language processing (NLP) modules, leveraging the ArmanEmo dataset to identify emotions, assess psychological states, and ensure safe, appropriate responses. Our evaluation of various models, including ParsBERT and XLM-RoBERTa, demonstrates effective emotion detection with accuracy up to 75.39%. Additionally, the system incorporates a Large Language Model (LLM) to generate messages. This chatbot serves as a promising solution for addressing the accessibility gap in mental health care and provides a scalable, language-inclusive platform for psychological support.

Language typology databases enhance multi-lingual Natural Language Processing (NLP) by improving model adaptability to diverse linguistic structures. The widely-used lang2vec toolkit integrates several such databases, but its coverage remains limited at 28.9%. Previous work on automatically increasing coverage predicts missing values based on features from other languages or focuses on single features, we propose to use textual data for better-informed feature prediction. To this end, we introduce a multi-lingual Part-of-Speech (POS) tagger, achieving over 70% accuracy across 1,749 languages, and experiment with external statistical features and a variety of machine learning algorithms. We also introduce a more realistic evaluation setup, focusing on likely to be missing typology features, and show that our approach outperforms previous work in both setups.

In this study, we present a novel approach to annotating bias and propaganda in social media data by leveraging topic modeling techniques. Utilizing the BERTopic tool, we performed topic modeling on the FIGNEWS Shared-task dataset, which initially comprised 13,500 samples. From this dataset, we identified 35 distinct topics and selected approximately 50 representative samples from each topic, resulting in a subset of 1,812 samples. These selected samples were meticulously annotated for bias and propaganda labels. Subsequently, we employed multiple methods like KNN, SVC, XGBoost, and RAG to develop a classifier capable of detecting bias and propaganda within social media content. Our approach demonstrates the efficacy of using topic modeling for efficient data subset selection and provides a robust foundation for improving the accuracy of bias and propaganda detection in large-scale social media datasets.

Mahsa Amini’s death shocked Iranian society. The effects of this event and the subsequent tragedies in Iran not only in realspace but also in cyberspace, including Twitter, were tremendous and unimaginable. We explore how Twitter has changed after Mahsa Amini’s death by analyzing the sentiments of Iranian users in the 90 days after this event. Additionally, we track the change in word meaning and each word’s neighboring words. Finally, we use word clustering methods for topic modeling.