ymoslem/acl-abstracts huggingface dataset:
Challenges in Adapting Multilingual LLMs to Low-Resource Languages using LoRA PEFT Tuning (2025)

Large Language Models (LLMs) have demonstrated remarkable multilingual capabilities, yet challenges persist in adapting these models for low-resource languages. In this study, we investigate the effects of Low-Rank Adaptation (LoRA) Parameter-Efficient Fine-Tuning (PEFT) on multilingual Gemma models for Marathi, a language with limited resources. Using a translated Alpaca dataset with 52,000 instruction-response pairs, our findings reveal that while evaluation metrics often show a performance decline post-fine-tuning, manual assessments frequently suggest that the fine-tuned models outperform their original counterparts. The observations indicate improvements in target language generation capabilities but a reduction in reasoning abilities following language adaptation. These results underscore the need for improved evaluation methodologies and the creation of high-quality native datasets to accurately assess language-specific model performance in low-resource settings.
1-800-SHARED-TASKS@NLU of Devanagari Script Languages 2025: Detection of Language, Hate Speech, and Targets using LLMs (2025)

This paper presents a detailed system description of our entry for the CHiPSAL 2025 challenge, focusing on language detection, hate speech identification, and target detection in Devanagari script languages. We experimented with a combination of large language models and their ensembles, including MuRIL, IndicBERT, and Gemma-2, and leveraged unique techniques like focal loss to address challenges in the natural understanding of Devanagari languages, such as multilingual processing and class imbalance. Our approach achieved competitive results across all tasks: F1 of 0.9980, 0.7652, and 0.6804 for Sub-tasks A, B, and C respectively. This work provides insights into the effectiveness of transformer models in tasks with domain-specific and linguistic challenges, as well as areas for potential improvement in future iterations.
AniSan@NLU of Devanagari Script Languages 2025: Optimizing Language Identification with Ensemble Learning (2025)

Identifying languages written in Devanagari script, including Hindi, Marathi, Nepali, Bhojpuri, and Sanskrit, is essential in multilingual contexts but challenging due to the high overlap between these languages. To address this, a shared task on {\textquotedblleft}Devanagari Script Language Identification{\textquotedblright} has been organized, with a dataset available for subtask A to test language identification models. This paper introduces an ensemble-based approach that combines mBERT, XLM-R, and IndicBERT models through majority voting to improve language identification accuracy across these languages. Our ensemble model has achieved an impressive accuracy of 99.68{\%}, outperforming individual models by capturing a broader range of language features and reducing model biases that often arise from closely related linguistic patterns. Additionally, we have fine-tuned other transformer models as part of a comparative analysis, providing further validation of the ensemble`s effectiveness. The results highlight the ensemble model`s ability in distinguishing similar languages within the Devanagari script, offering a promising approach for accurate language identification in complex multilingual contexts.
byteSizedLLM@NLU of Devanagari Script Languages 2025: Hate Speech Detection and Target Identification Using Customized Attention BiLSTM and XLM-RoBERTa Base Embeddings (2025)

This paper presents a novel approach to hate speech detection and target identification across Devanagari-script languages, with a focus on Hindi and Nepali. Leveraging an Attention BiLSTM-XLM-RoBERTa architecture, our model effectively captures language-specific features and sequential dependencies crucial for multilingual natural language understanding (NLU). In Task B (Hate Speech Detection), our model achieved a Macro F1 score of 0.7481, demonstrating its robustness in identifying hateful content across linguistic variations. For Task C (Target Identification), it reached a Macro F1 score of 0.6715, highlighting its ability to classify targets into {\textquotedblleft}individual,{\textquotedblright} {\textquotedblleft}organization,{\textquotedblright} and {\textquotedblleft}community{\textquotedblright} with high accuracy. Our work addresses the gap in Devanagari-scripted multilingual hate speech analysis and sets a benchmark for future research in low-resource language contexts.
byteSizedLLM@NLU of Devanagari Script Languages 2025: Language Identification Using Customized Attention BiLSTM and XLM-RoBERTa base Embeddings (2025)

This study explores the challenges of natural language understanding (NLU) in multilingual contexts, focusing on Devanagari-scripted languages such as Nepali, Marathi, Sanskrit, Bhojpuri, and Hindi. Language identification within these languages is complex due to their structural and lexical similarities. We present a hybrid Attention BiLSTM-XLM-RoBERTa model, achieving a state-of-the-art F1 score of 0.9974 on the test set, despite limited resources. Our model effectively distinguishes between closely related Devanagari-scripted languages, providing a solid foundation for context-aware NLU systems that enhance language-specific processing and promote inclusive digital interactions across diverse linguistic communities.
CUET\_Big\_O@NLU of Devanagari Script Languages 2025: Identifying Script Language and Detecting Hate Speech Using Deep Learning and Transformer Model (2025)

Text-based hate speech has been prevalent and is usually used to incite hostility and violence. Detecting this content becomes imperative, yet the task is challenging, particularly for low-resource languages in the Devanagari script, which must have the extensive labeled datasets required for effective machine learning. To address this, a shared task has been organized for identifying hate speech targets in Devanagari-script text. The task involves classifying targets such as individuals, organizations, and communities and identifying different languages within the script. We have explored several machine learning methods such as LR, SVM, MNB, and Random Forest, deep learning models using CNN, BiLSTM, GRU, CNN+BiLSTM, and transformer-based models like Indic-BERT, m-BERT, Verta-BERT, XLM-R, and MuRIL. The CNN with BiLSTM yielded the best performance (F1-score of 0.9941), placing the team 13th in the competition for script identification. Furthermore, the fine-tuned MuRIL-BERT model resulted in an F1 score of 0.6832, ranking us 4th for detecting hate speech targets.
CUET\_HateShield@NLU of Devanagari Script Languages 2025: Transformer-Based Hate Speech Detection in Devanagari Script Languages (2025)

Social media has become a vital platform for information exchange and free expression, yet its open nature also contributes to the spread of harmful content, including hate speech, cyberbullying, and offensive language, posing serious risks to societal well-being. Such content is linked to adverse impacts, including mental health issues. This study aims to develop an automated system for detecting hate speech in Devanagari script languages, enabling efficient moderation and prompt intervention. Our approach utilizes a fine-tuned transformer model to classify offensive content. We experimented with various machine learning (Logistic Regression, SVM, Ensemble methods) and deep learning architectures (CNN, BiLSTM, CNN-BiLSTM) alongside transformer-based models (Indic-SBERT, m-BERT, MuRIL, Indic-SBERT, XLM-R). Notably, the fine-tuned XLM-Roberta model achieved the highest performance, reaching a macro-average F1-score of 0.74, demonstrating its efficacy in detecting hate speech in Devanagari script languages. However, the model we submitted achieved a macro-average F1-score of 0.73, securing 13th place in the subtask.
CUET\_INSights@NLU of Devanagari Script Languages 2025: Leveraging Transformer-based Models for Target Identification in Hate Speech (2025)

Hate speech detection in multilingual content is a challenging problem especially when it comes to understanding the specific targets of hateful expressions. Identifying the targets of hate speech whether directed at individuals, organizations or communities is crucial for effective content moderation and understanding the context. A shared task on hate speech detection in Devanagari Script Languages organized by CHIPSAL@COLING 2025 allowed us to address the challenge of identifying the target of hate speech in the Devanagari Script Language. For this task, we experimented with various machine learning (ML) and deep learning (DL) models including Logistic Regression, Decision Trees, Random Forest, SVM, CNN, LSTM, BiLSTM, and transformer-based models like MiniLM, m-BERT, and Indic-BERT. Our experiments demonstrated that Indic-BERT achieved the highest F1-score of 0.69, ranked 3rd in the shared task. This research contributes to advancing the field of hate speech detection and natural language processing in low-resource languages.
CUFE@NLU of Devanagari Script Languages 2025: Language Identification using fastText (2025)

Language identification is a critical area of research within natural language processing (NLP), particularly in multilingual contexts where accurate language detection can enhance the performance of various applications, such as machine translation, content moderation, and user interaction systems. This paper presents a language identification system developed using fastText. In the CHIPSAL@COLING 2025 Task on Devanagari Script Language Identification, the proposed method achieved first place, with an F1 score of 0.9997.
Dll5143A@NLU of Devanagari Script Languages 2025: Detection of Hate Speech and Targets Using Hierarchical Attention Network (2025)

Hate speech poses a significant challenge on social networks, particularly in Devanagari scripted languages, where subtle expressions can lead to harmful narratives. This paper details our participation in the {\textquotedblleft}Shared Task on Natural Language Understanding of Devanagari Script Languages{\textquotedblright} at CHIPSAL@COLING 2025, addressing hate speech detection and target identification. In Sub-task B, we focused on classifying the text either hate or non-hate classified text to determine the presence of hate speech, while Sub-task C focused on identifying targets, such as individuals, organizations, or communities. We utilized the XLM-RoBERTa model as our base and explored various adaptations, including Adaptive Weighting and Gated Adaptive Weighting methods. Our results demonstrated that the Hierarchical Gated adaptive weighting model achieved 86{\%} accuracy in hate speech detection with a macro F1 score of 0.72, particularly improving performance for minority class detection. For target detection, the same model achieved 75{\%} accuracy and a 0.69 macro F1 score. Our proposed architecture demonstrated competitive performance, ranking 8th in Subtask B and 11th in Subtask C among all participants.
DSLNLP@NLU of Devanagari Script Languages 2025: Leveraging BERT-based Architectures for Language Identification, Hate Speech Detection and Target Classification (2025)

The rapid rise of social media has emphasized the spread of harmful and hateful content, making it challenging for its identification. Contextual semantics is very important as prior studies present that context level semantics is a more trustworthy indicator of hatefulness than word level semantics for detecting hate speech. This paper attempts to check the usability of transformer-based models for the identification of hate speech on code-mixed datasets, which includes Google-MuRIL, LaBSE, XLMRoberta-base, mbert and distil-mbert. The above is largely due to its ability for high-level representations of complex and context-dense meaning. Besides this, we experiment on ensemble approach that covers all of the above models to reach out for an even higher level of performance in detection. The experiment results show the best performing macro F1-scores are reported in case of MuRIL in comparison to other implemented models.
IITR-CIOL@NLU of Devanagari Script Languages 2025: Multilingual Hate Speech Detection and Target Identification in Devanagari-Scripted Languages (2025)

This work focuses on two subtasks related to hate speech detection and target identification in Devanagari-scripted languages, specifically Hindi, Marathi, Nepali, Bhojpuri, and Sanskrit. Subtask B involves detecting hate speech in online text, while Subtask C requires identifying the specific targets of hate speech, such as individuals, organizations, or communities. We develop a deep neural network built on the pretrained multilingual transformer model {\textquoteleft}ia-multilingual-transliterated-roberta' by IBM, optimized for classification tasks in multilingual and transliterated contexts. The model leverages contextualized embeddings to handle linguistic diversity, with a classifier head for binary classification. We received 88.40{\%} accuracy in Subtask B and 66.11{\%} accuracy in Subtask C, in the test set.
LLMsAgainstHate@NLU of Devanagari Script Languages 2025: Hate Speech Detection and Target Identification in Devanagari Languages via Parameter Efficient Fine-Tuning of LLMs (2025)

The detection of hate speech has become increasingly important in combating online hostility and its real-world consequences. Despite recent advancements, there is limited research addressing hate speech detection in Devanagari-scripted languages, where resources and tools are scarce. While large language models (LLMs) have shown promise in language-related tasks, traditional fine-tuning approaches are often infeasible given the size of the models. In this paper, we propose a Parameter Efficient Fine tuning (PEFT) based solution for hate speech detection and target identification. We evaluate multiple LLMs on the Devanagari dataset provided by Thapa et al. (2025), which contains annotated instances in 2 languages - Hindi and Nepali. The results demonstrate the efficacy of our approach in handling Devanagari-scripted content. Code will be made publicly available on GitHub following acceptance.
MDSBots@NLU of Devanagari Script Languages 2025: Detection of Language, Hate Speech, and Targets using MURTweet (2025)

In multilingual contexts, an automated system for accurate language identification, followed by hate speech detection and target identification, plays a critical role in processing low-resource hate speech data and mitigating its negative impact. This paper presents our approach to the three subtasks in the Shared Task on Natural Language Understanding of Devanagari Script Languages at CHIPSAL@COLING 2025: (i) Language Identification, (ii) Hate Speech Detection, and (iii) Target Identification. Both classical machine learning and multilingual transformer models were explored, where MuRIL Large, trained on undersampled data for subtasks A and B outperformed the classical models. For subtask C, the Hybrid model trained on augmented data achieved superior performance over classical and transformer-based approaches. The top-performing models, named MURTweet for subtasks A and B and NER-MURTweet for subtask C, secured sixth, third, and first rank respectively, in the competition.
Nepali Transformers@NLU of Devanagari Script Languages 2025: Detection of Language, Hate Speech and Targets (2025)

The Devanagari script, an Indic script used by a diverse range of South Asian languages, presents a significant challenge in Natural Language Processing (NLP) research. The dialect and language variation, complex script features, and limited language-specific tools make development difficult. This shared task aims to address this challenge by bringing together researchers and practitioners to solve three key problems: Language identification, Hate speech detection, and Targets of Hate speech identification. The selected languages- Hindi, Nepali, Marathi, Sanskrit, and Bhojpuri- are widely used in South Asia and represent distinct linguistic structures. In this work, we explore the effectiveness of both machine-learning models and transformer-based models on all three sub-tasks. Our results demonstrate strong performance of the multilingual transformer model, particularly one pre-trained on domain-specific social media data, across all three tasks. The multilingual RoBERTa model, trained on the Twitter dataset, achieved a remarkable accuracy and F1-score of 99.5{\%} on language identification (Task A), 88.3{\%} and 72.5{\%} on Hate Speech detection (Task B), and 68.6{\%} and 61.8{\%} on Hate Speech Target Classification (Task C).
NLPineers@ NLU of Devanagari Script Languages 2025: Hate Speech Detection using Ensembling of BERT-based models (2025)

This paper explores hate speech detection in Devanagari-scripted languages, focusing on Hindi and Nepali, for Subtask B of the CHIPSAL@COLING 2025 Shared Task. Using a range of transformer-based models such as XLM-RoBERTa, MURIL, and IndicBERT, we examine their effectiveness in navigating the nuanced boundary between hate speech and free expression. Our best performing model, implemented as ensemble of multilingual BERT models achieve Recall of 0.7762 (Rank 3/31 in terms of recall) and F1 score of 0.6914 (Rank 17/31). To address class imbalance, we used backtranslation for data augmentation, and cosine similarity to preserve label consistency after augmentation. This work emphasizes the need for hate speech detection in Devanagari-scripted languages and presents a foundation for further research. We plan to release the code upon acceptance.
One\_by\_zero@ NLU of Devanagari Script Languages 2025: Target Identification for Hate Speech Leveraging Transformer-based Approach (2025)

People often use written words to spread hate aimed at different groups that cannot be practically detected manually. Therefore, developing an automatic system capable of identifying hate speech is crucial. However, creating such a system in a low-resourced languages (LRLs) script like Devanagari becomes challenging. Hence, the Devanagari script has organized a shared task targeting hate speech identification. This work proposes a pre-trained transformer-based model to identify the target of hate speech, classifying it as directed toward an individual, organization, or community. We performed extensive experiments, exploring various machine learning (LR, SVM, and ensemble), deep learning (CNN, LSTM, CNN+BiLSTM), and transformer-based models (IndicBERT, mBERT, MuRIL, XLM-R) to identify hate speech. Experimental results indicate that the IndicBERT model achieved the highest performance among all other models, obtaining a macro F1-score of 0.6785, which placed the team 6th in the task.
Paramananda@NLU of Devanagari Script Languages 2025: Detection of Language, Hate Speech and Targets using FastText and BERT (2025)

This paper presents a comparative analysis of FastText and BERT-based approaches for Natural Language Understanding (NLU) tasks in Devanagari script languages. We evaluate these models on three critical tasks: language identification, hate speech detection, and target identification across five languages: Nepali, Marathi, Sanskrit, Bhojpuri, and Hindi. Our experiments, although with raw tweet dataset but extracting only devanagari script, demonstrate that while both models achieve exceptional performance in language identification (F1 scores {\ensuremath{>}} 0.99), they show varying effectiveness in hate speech detection and target identification tasks. FastText with augmented data outperforms BERT in hate speech detection (F1 score: 0.8552 vs 0.5763), while BERT shows superior performance in target identification (F1 score: 0.5785 vs 0.4898). These findings contribute to the growing body of research on NLU for low-resource languages and provide insights into model selection for specific tasks in Devanagari script processing.
SKPD Emergency @ NLU of Devanagari Script Languages 2025: Devanagari Script Classification using CBOW Embeddings with Attention-Enhanced BiLSTM (2025)

Devanagari script, encompassing languages such as Nepali, Marathi, Sanskrit, Bhojpuri and Hindi, involves challenges for identification due to its overlapping character sets and lexical characteristics. To address this, we propose a method that utilizes Continuous Bag of Words (CBOW) embeddings integrated with attention-enhanced Bidirectional Long Short-Term Memory (BiLSTM) network. Our methodology involves meticulous data preprocessing and generation of word embeddings to better the model`s ability. The proposed method achieves an overall accuracy of 99{\%}, significantly outperforming character level identification approaches. The results reveal high precision across most language pairs, though minor classification confusions persist between closely related languages. Our findings demonstrate the robustness of the CBOW-BiLSTM model for Devanagari script classification and highlights the importance of accurate language identification in preserving linguistic diversity in multilingual environments. Keywords: Language Identification, Devanagari Script, Natural Language Processing, Neural Networks
An Annotated Error Corpus for Esperanto (2025)

This paper presents and evaluates a new multi-genre error corpus for (written) Esperanto, EspEraro, building on both learner, news and internet data and covering both ordinary spelling errors and real-word errors such as grammatical and word choice errors. Because the corpus has been annotated not only for errors, error types and corrections, but also with Constraint Grammar (CG) tags for part-of-speech, inflection, affixation, syntactic function, dependency and semantic class, it allows users to linguistically contextualize errors and to craft and test CG rules aiming at the recognition and/or correction of the various error types covered in the corpus. The resource was originally created for regression-testing a newly developed spell- and grammar checker, and contains about 75,000 tokens ( 4,000 sentences), with 3,330 tokens annotated for one or more errors and a combined correction suggestion. We discuss the different error types and evaluate their weight in the corpus. Where relevant, we explain the role of Constraint Grammar (CG) in the identification and correction of the individual error types.
Rule-based Surface Realization of Romanian Weak Pronouns (2025)

Due to its reliance on context and intricate grammatical rules, the Romanian weak pronoun system presents a challenge not only for language learners {--} both native and non-native speakers {--} but also for linguistic description and computational processing. The present work addresses the challenges of Romanian weak pronouns from a computational processing perspective. Accordingly, it has three main goals: (1) to present the implementation of a rule-based model for generating contextually accurate surface forms of Romanian weak pronouns, (2) to describe the compilation of a database of relevant inputs for testing surface realization, and (3) to test the effectiveness of the model. This serves as a proof of concept, demonstrating both the transparency and the effectiveness of the model when based on an appropriate linguistic description.