ymoslem/acl-abstracts huggingface dataset:
DadmaTools V2: an Adapter-Based Natural Language Processing Toolkit for the Persian Language (2025)

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.
Developing an Informal-Formal Persian Corpus: Highlighting the Differences between Two Writing Styles (2025)

Informal language is a style of spoken or written language frequently used in casual conversations, social media, weblogs, emails and text messages. In informal writing, the language undergoes some lexical and/or syntactic changes varying among different languages. Persian is one of the languages with many differences between its formal and informal styles of writing, thus developing informal language processing tools for this language seems necessary. In the present paper, the methodology in building a parallel corpus of 50,000 sentence pairs with alignments in the word/phrase level is described. The resulting corpus has about 530,000 alignments and a dictionary containing 49,397 word and phrase pairs. The observed differences between formal and informal writing are explained in detail.
Boosting Sentiment Analysis in Persian through a GAN-Based Synthetic Data Augmentation Method (2025)

This paper presents a novel Sentiment Analysis (SA) dataset in the low-resource Persian language including a data augmentation technique using Generative Adversarial Networks (GANs) to generate synthetic data, boosting the volume and variety of data, for achieving state-of-the-art performance. We propose a novel annotated SA dataset, called Senti-Persian, made of 67,743 public comments on movie reviews from Iranian websites (Namava, Filimo and Aparat) and social media (YouTube, Twitter and Instagram). These reviews are labeled with one of the polarity labels, namely positive, negative, and neutral. Our study includes a novel text augmentation model based on GANs. The generator was designed following the linguistic properties of Persian linguistics, while the discriminator was designed based on the cosine similarity of the vectorized original and generated sentences, i.e. using CLS-embedings of BERT. A SA task applied on both collected and augmented datasets for which we observed a significant improvement in the accuracy from 88.4{\%} for the original dataset to the 96{\%} when augmented with synthetic data. Senti-Parsian dataset including the original and the augmented ones will be available on github.
Psychological Health Chatbot, Detecting and Assisting Patients in their Path to Recovery (2025)

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.
A Derivational ChainBank for Modern Standard Arabic (2025)

We introduce the new concept of an Arabic Derivational Chain Bank (CHAINBANK) to leverage the relationship between form and meaning in modeling Arabic derivational morphology. We constructed a knowledge graph network of abstract patterns and their derivational relations, and aligned it with the lemmas of the CAMELMORPH morphological analyzer database. This process produced chains of derived words' lemmas linked to their correspond- ing lemma bases through derivational relations, encompassing 23,333 derivational connections. The CHAINBANK is publicly available.1
Sentiment Analysis of Arabic Tweets Using Large Language Models (2025)

In the digital era, sentiment analysis has become an indispensable tool for understanding public sentiments, optimizing market strategies, and enhancing customer engagement across diverse sectors. While significant advancements have been made in sentiment analysis for high-resource languages such as English, French, etc. This study focuses on Arabic, a low-resource language, to address its unique challenges like morphological complexity, diverse dialects, and limited linguistic resources. Existing works in Arabic sentiment analysis have utilized deep learning architectures like LSTM, BiLSTM, and CNN-LSTM, alongside embedding techniques such as Word2Vec and contextualized models like ARABERT. Building on this foundation, our research investigates sentiment classification of Arabic tweets, categorizing them as positive or negative, using embeddings derived from three large language models (LLMs): Universal Sentence Encoder (USE), XLM-RoBERTa base (XLM-R base), and MiniLM-L12-v2. Experimental results demonstrate that incorporating emojis in the dataset and using the MiniLM embeddings yield an accuracy of 85.98{\%}. In contrast, excluding emojis and using embeddings from the XLM-R base resulted in a lower accuracy of 78.98{\%}. These findings highlight the impact of both dataset composition and embedding techniques on Arabic sentiment analysis performance.
Evaluating Large Language Models on Health-Related Claims Across Arabic Dialects (2025)

While the Large Language Models (LLMs) have been popular in different tasks, their capability to handle health-related claims in diverse linguistic and cultural contexts, such as Arabic dialects, Saudi, Egyptian, Lebanese, and Moroccan has not been thoroughly explored. To this end, we develop a comprehensive evaluation framework to assess how LLMs particularly GPT-4 respond to health-related claims. Our framework focuses on measuring factual accuracy, consistency, and cultural adaptability. It introduces a new metric, the {\textquotedblleft}Cultural Sensitivity Score{\textquotedblright}, to evaluate the model`s ability to adjust responses based on dialectal differences. Additionally, the reasoning patterns used by the models are analyzed to assess their effectiveness in engaging with claims across these dialects. Our findings highlight that while LLMs excel in recognizing true claims, they encounter difficulties with mixed and ambiguous claims, especially in underrepresented dialects. This work underscores the importance of dialect-specific evaluations to ensure accurate, contextually appropriate, and culturally sensitive responses from LLMs in real-world applications.
Can LLMs Verify Arabic Claims? Evaluating the Arabic Fact-Checking Abilities of Multilingual LLMs (2025)

Large language models (LLMs) have demonstrated potential in fact-checking claims, yet their capabilities in verifying claims in multilingual contexts remain largely understudied. This paper investigates the efficacy of various prompting techniques, viz. Zero-Shot, English Chain-of-Thought, Self-Consistency, and Cross-Lingual Prompting, in enhancing the fact-checking and claim-verification abilities of LLMs for Arabic claims. We utilize 771 Arabic claims sourced from the X-fact dataset to benchmark the performance of four LLMs. To the best of our knowledge, ours is the first study to benchmark the inherent Arabic fact-checking abilities of LLMs stemming from their knowledge of Arabic facts, using a variety of prompting methods. Our results reveal significant variations in accuracy across different prompting methods. Our findings suggest that Cross-Lingual Prompting outperforms other methods, leading to notable performance gains.
Can LLMs Translate Cultural Nuance in Dialects? A Case Study on Lebanese Arabic (2025)

Machine Translation (MT) of Arabic-script languages presents unique challenges due to their vast linguistic diversity and lack of standardization. This paper focuses on the Lebanese dialect, investigating the effectiveness of Large Language Models (LLMs) in handling culturally-aware translations. We identify critical limitations in existing Lebanese-English parallel datasets, particularly their non-native nature and lack of cultural context. To address these gaps, we introduce a new culturally-rich dataset derived from the Language Wave (LW) podcast. We evaluate the performance of LLMs: Jais, AceGPT, Cohere, and GPT-4 models against Neural Machine Translation (NMT) systems: NLLB-200, and Google Translate. Our findings reveal that while both architectures perform similarly on non-native datasets, LLMs demonstrate superior capabilities in preserving cultural nuances when handling authentic Lebanese content. Additionally, we validate xCOMET as a reliable metric for evaluating the quality of Arabic dialect translation, showing a strong correlation with human judgment. This work contributes to the growing field of Culturally-Aware Machine Translation and highlights the importance of authentic, culturally representative datasets in advancing low-resource translation systems.
Automated Generation of Arabic Verb Conjugations with Multilingual Urdu Translation: An NLP Approach (2025)

This paper presents a rule-based automated system for generating both Arabic verb conjugations and their corresponding Urdu translations. The system processes triliteral, non-weak Arabic roots across key tenses Past Simple, Past Simple Negative, Present Simple, and Present Simple Negative. Addressing the challenges posed by Arabic morphology, our rule-based approach applies patterns and morphological rules to accurately produce verb conjugations, capturing essential grammatical variations in gender, number, and person. Simultaneously, the system generates Urdu translations using predefined patterns that is aligned with the grammatical nuances of Arabic, ensuring semantic consistency. As the first system of its kind, it uniquely provides a cross-lingual resource that bridges two linguistically similar but distinct languages. By focusing on rule based precision and dual-language outputs, it addresses critical gaps in NLP resources, serving as a valuable tool for linguists, educators, and NLP researchers in academic and religious contexts where Arabic and Urdu coexist.
Evaluation of Large Language Models on Arabic Punctuation Prediction (2025)

The linguistic inclusivity of Large Language Models (LLMs) such as ChatGPT, Gemni, JAIS, and AceGPT has not been sufficiently explored, particularly in their handling of low-resource languages like Arabic compared to English. While these models have shown impressive performance across various tasks, their effectiveness in Arabic remains under-examined. Punctuation, critical for sentence structure and comprehension in tasks like speech analysis, synthesis, and machine translation, requires precise prediction. This paper assesses seven LLMs: GPT4-o, Gemni1.5, JAIS, AceGPT, SILMA, ALLaM, and CommandR+ for Arabic punctuation prediction. Additionally, the performance of fine-tuned AraBERT is compared with these models in zero-shot and few-shot settings using a proposed Arabic punctuation prediction corpus of 10,044 sentences. The experiments demonstrate that while AraBERT performs well for specific punctuation marks, LLMs show significant promise in zero-shot learning, with further improvements in few-shot scenarios. These findings highlight the potential of LLMs to enhance the automation and accuracy of Arabic text processing.
Evaluating RAG Pipelines for Arabic Lexical Information Retrieval: A Comparative Study of Embedding and Generation Models (2025)

This paper investigates the effectiveness of retrieval-augmented generation (RAG) pipelines, focusing on the Arabic lexical information retrieval. Specifically, it analyzes how embedding models affect the recall of Arabic lexical information and evaluates the ability of large language models (LLMs) to produce accurate and contextually relevant answers within the RAG pipelines. We examine a dataset of over 88,000 words from the Riyadh dictionary and evaluate the models using metrics such as Top-K Recall, Mean Reciprocal Rank (MRR), F1 Score, Cosine Similarity, and Accuracy. The research assesses the capabilities of several embedding models, including E5-large, BGE, AraBERT, CAMeLBERT, and AraELECTRA, highlighting a disparity in performance between sentence embeddings and word embeddings. Sentence embedding with E5 achieved the best results, with a Top-5 Recall of 0.88, and an MRR of 0.48. For the generation models, we evaluated GPT-4, GPT-3.5, SILMA-9B, Gemini-1.5, Aya-8B, and AceGPT-13B based on their ability to generate accurate and contextually appropriate responses. GPT-4 demonstrated the best performance, achieving an F1 score of 0.90, an accuracy of 0.82, and a cosine similarity of 0.87. Our results emphasize the strengths and limitations of both embedding and generation models in Arabic tasks.
Conversational XAI and Explanation Dialogues (2024)

My main research interest is human-centric explainability, i.e., making language models more interpretable by building applications that lower the barrier of entry to explanations. I am enthusiastic about interactive systems that pique the interest of more people beyond just the experts to learn about the inner workings of language models. My hypothesis is that users of language model applications and dialogue systems are more satisfied and trustworthy if they can look behind the curtain and get easy access to explanations of their behavior.
Enhancing Emotion Recognition in Spoken Dialogue Systems through Multimodal Integration and Personalization (2024)

My research interests focus on multimodal emotion recognition and personalization in emotion recognition tasks. In multimodal emotion recognition, existing studies demonstrate that integrating various data types like speech, text, and video enhances accuracy. However, real-time constraints and high dataset costs limit their practical application. I propose constructing a multimodal emotion recognition model by combining available unimodal datasets. In terms of personalization, traditional discrete emotion labels often fail to capture the complexity of human emotions. Although recent methods embed speaker characteristics to boost prediction accuracy, they require extensive retraining. I introduce continuous prompt tuning, which updates only the speaker prompts while keeping the speech encoder weights fixed, enabling the addition of new speaker data without additional retraining. This paper discusses these existing research gaps and presents novel approaches to address them, aiming to significantly improve emotion recognition in spoken dialogue systems.
Towards Personalisation of User Support Systems. (2024)

My research interests lie on the development of advanced user support systems, emphasizing the enhancement of user engagement and system effectiveness. The field of user support systems aims to help users accomplish complex tasks efficiently while ensuring a pleasant and intuitive interaction experience. I explore how to incorporate engaging and context-appropriate assistance into these systems to make the task completion process more effective and enjoyable for users.
Social Agents for Positively Influencing Human Psychological States (2024)

My research interest lies in the realm of social interactive agents, specifically in the development of social agents for positively influencing human psychological states. This interdisciplinary field merges elements of artificial intelligence, psychology, and human-computer interaction. My work integrates psychological theories with dialogue system technologies, including rule-based systems and large language models (LLMs). The core aim of my work is to leverage these systems to promote mental well-being and enhance user experiences in various contexts. At YRRSDS 2024, I plan to discuss several intriguing topics in spoken dialogue systems (SDS), including implementing psychological theories into SDS, assessing human-agent rapport without direct human evaluation, and conducting SDS evaluations using other SDSs. These topics promise to stimulate insightful and engaging discussions during the roundtable at YRRSDS.
Personalized Topic Transition for Dialogue System (2024)

In our research, we aim to achieve SDS capable of generating responses considering user preferences. While users have individual topic preferences, existing SDSs do not adequately consider such information. With the development of LLMs, SDSs are expected to be implemented in various tasks, including coexisting with humans in robotic applications. To become better partners with humans, systems are anticipated to memorize user preferences and utilize them in their response generation. Our future reserarch aim to realize SDSs that can remember and complement user information through dialogue, enabling personalized interactions. In YRRSDS, The author would like to propose the following topics for discussion. 1. What is the necessity of SDSs aimed specifically at dialogue rather than being just user interfaces? What do general users need from SDSs through conversation? 2. The relationship between SDSs and users: Should SDSs act just as agents, or should they aim to become like friends or family? 3. Privacy in conversational content. Nowadays, many SDS applications operate online via APIs, but is this preferable from a privacy perspective? If it is not preferable, how can this issue be resolved?
Elucidation of Psychotherapy and Development of New Treatment Methods Using AI (2024)

My research theme is to develop an optimal analytical model for various information generated during therapy using multimodal data in psychotherapy, to elucidate the process of psychotherapy, and to create an AI therapist to develop a new psychotherapy. In this context, I would like to participate in the Young Researchers' Roundtable on Spoken Dialogue Systems because I believe that I can broaden my research horizons by participating and discussing with various young researchers.
Assessing Interactional Competence with Multimodal Dialog Systems (2024)

My research interests lie in multimodal dialog systems, especially in turn-taking and the understanding and generation of non-verbal cues. I am also interested in bringing dialog system research into industry, and making virtual agents practical in real world setting. I have been working on the Intelligent Language Learning Assistant (InteLLA) system, a virtual agent designed to provide fully automated English proficiency assessments through oral conversations. This project is driven by the practical need to address the lack of opportunities for second-language learners to assess and practice their conversation skills.
Faithfulness of Natural Language Generation (2024)

In this position paper, I present my research interest in the faithfulness of natural language generation, i.e. the adherence to the data provided by a user or the dialog state. I motivate the task and present my progress and plans on the topic. I propose my position on the future of research dialog systems and share topics I would like to discuss during the roundtables.
Knowledge-Grounded Dialogue Systems for Generating Interesting and Engaging Responses (2024)

My research interests lie in the area of building a dialogue system to generate interesting and entertaining responses, with a particular focus on knowledge-grounded dialogue systems. Study of open-domain dialogue systems seeks to maximize user engagement by enhancing specific dialogue skills. To achieve this goal, much research has focused on the generation of empathetic responses, personality-based responses, and knowledge-grounded responses. In addition, interesting and entertaining responses from the open-domain dialogue systems can increase user satisfaction and engagement due to their diversity and ability to attract the user`s interest. It has also been observed in task-oriented dialogue, user engagement can be increased by incorporating interesting responses into the dialogue. For example, methods have been proposed to incorporate interesting responses into spoken dialogue systems (SDSs) that support the execution of complex tasks and provide a pleasant and enjoyable experience for the user. However, even in the case of interesting responses, if the dialogue is incoherent, user engagement is likely to be significantly reduced. To create a dialogue system that is consistent and interesting in a dialogue context, I am working on using knowledge-grounded response generation methods to select interesting knowledge that is relevant to the dialogue context and to make responses that are based on that knowledge.
Towards a Dialogue System That Can Take Interlocutors' Values into Account (2024)

In this position paper, I present my research interests regarding the dialogue systems that can reflect the interlocutor`s values, such as their way of thinking and perceiving things. My work focuses on two main aspects: dialogue systems for eliciting the interlocutor`s values and methods for understanding the interlocutor`s values from narratives. Additionally, I discuss the abilities required for Spoken Dialogue Systems (SDSs) that can converse with the same user multiple times. Finally, I suggest topics for discussion regarding an SDS as a personal assistant for everyday use.
Multimodal Spoken Dialogue System with Biosignals (2024)

The dominance of large language models has forced the transformation of research directions in many domains. The growth speed of large-scale models and the knowledge acquired have reached incredible levels. Thus, researchers must have the ability and foresight to adapt to a rapidly changing environment. In this position paper, the author introduces research interests and discusses their relationships from the perspective of spoken dialogue systems. In particular, the fields of multimodal processing and affective computing are introduced. Additionally, the effects of large language models on spoken dialogue systems research and topics for discussion are presented.
Timing Sensitive Turn-Taking in Spoken Dialogue Systems Based on User Satisfaction (2024)