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共 27256 条记录，以下是331-340 订阅

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Compression Strategies for Massive natural language Generation Models 5th

Compression Strategies for Massive Natural Language Generat...

引用

5th EAI international conference on Cognitive Computing and Cyber Physical Systems, IC4S 2024

作者： Dayal, Abhinav Angara, Jayasri Gupta, Sumit Saripalle, Ravi Shankar Vishnu Institute of Technology Bhimavaram India GITAM University Visakhapatnam India Gayatri Vidya Parishad College of Engineering Visakhapatnam India

ISBN: (纸本)9783031770807

This paper presents a comprehensive review and proposed strategy for compressing massive natural language Generation (NLG) models focused on enhancing the efficiency of transformer-based models for deployment in real-time online applications. The review examines a range of compression techniques, including pruning, quantization, distillation, and knowledge distillation, explaining their roles in reducing the computational and memory requirements of these models. This reduction is crucial in mitigating deployment costs and latency problems, thus improving user experience. The unique complexities of transformer-based models, such as their sequential nature and attention mechanism, necessitate that the compression strategy applied be task-specific to be effective. The outcomes of this study serve as a valuable guide for NLG model developers wanting to optimize their models for online deployment. The effectiveness of these methods is discussed in terms of numerical performance indices such as latency reduction, memory footprint reduction, and efficiency improvement. By leveraging these strategies, researchers can develop more efficient and cost-effective NLG models. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications engineering 2025.

关键词： Efficiency

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Customer-Based Opinion Analysis Using Clustering and Classification Techniques 5th

Customer-Based Opinion Analysis Using Clustering and Classif...

引用

5th international conference on Data Science, Machine Learning and Applications

作者： Ahad, Abdul Riyazuddin Sadiq, Jaffar Raju, Basava Lakshmi, Rama Anurag Univ Dept AI Hyderabad India Anurag Univ Dept IT Hyderabad India Sreenidhi Inst Sci & Tech Dept CSE DS Hyderabad India SRKR Engn Coll Dept CSD Bhimavaram India

ISBN: (纸本)9789819780334;9789819780310;9789819780303

This article accentuates the opinion examination in view of the order of flipkart client surveys. Feeling Examination known as Feeling Man-made consciousness or Assessment Mining. Computerized reasoning (artificial intelligence) alludes to the efficient recognizable proof, extraction, measurement, and investigation of emotional states and abstract information utilizing regular language handling and text examination. An application challenge in message mining and computational phonetics research is opinion examination of item audits. Here, the relationship between Flipkart item audits and the rating of the items given by the clients is to be considered. Different machine learning methods are utilized. In the first place, the surveys can be changed into vector portrayal utilizing various strategies, i.e., Sack of-words, and TF-IDF. Then, train the system by applying the Strategic Relapse, XGBoost, and Decision Trees. From that point forward, assess the models utilizing F1-Score. Thus, opinion examination is unmistakably applied to audits, review reactions, web and virtual entertainment, and medical care assets for purposes going from showcasing to client assistance to clinical medication.

关键词： natural language processing Opinion analysis Logistic regression K-mean clustering SVM classification

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Leveraging AI to Ensure Authenticity in Student Assignments: A knowledge-Based Validation and Evaluation Framework 5

Leveraging AI to Ensure Authenticity in Student Assignments:...

引用

5th international conference on Advanced Research in Computing, ICARC 2025

作者： Hondarangala, Chinthaka Wickramaarachchi, Dilani University of Kelaniya Department of Industrial Management Sri Lanka

ISBN: (纸本)9798331530983

This research explores the development of an AI-driven assessment system designed to evaluate student knowledge while minimizing cheating in educational settings. The system operates in two distinct phases. In Phase 1, AI-generated, contextually relevant questions based on student-submitted assignments assess whether the student has genuinely engaged with the content or used external tools, such as generative AI, without proper understanding. In Phase 2, the system delves deeper into conceptual understanding by aligning submissions with course-specific materials, evaluating relevance, depth, and originality. A fine-tuned large language model (LLM), trained on domain-specific data, enables precise evaluation against academic standards. Results indicate that students demonstrated marked improvements in understanding and performance, validating the effectiveness of the two-phase model. This model encouraged deeper engagement and comprehension, increasing students' knowledge of the material. By addressing the shortcomings of traditional assessment methods, this research introduces a model that reduces the likelihood of students blindly submitting assignments without truly understanding the content. © 2025 IEEE.

关键词： natural language processing systems

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Breaking Through the Spike: Spike Window Decoding for Accelerated and Precise Automatic Speech Recognition

Breaking Through the Spike: Spike Window Decoding for Accele...

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2025 IEEE international conference on Acoustics, Speech, and Signal processing, ICASSP 2025

作者： Zhang, Wei Zhang, Tian-Hao Luo, Chao Zhou, Hui Yang, Chao Qian, Xinyuan Yin, Xu-Cheng *** GROUP Shanghai China School of Computer and Communication Engineering University of Science and Technology Beijing Beijing China

ISBN: (纸本)9798350368741

Recently, end-to-end automatic speech recognition has become the mainstream approach in both industry and academia. To optimize system performance in specific scenarios, the Weighted Finite-State Transducer (WFST) is extensively used to integrate acoustic and language models, leveraging its capacity to implicitly fuse language models within static graphs, thereby ensuring robust recognition while also facilitating rapid error correction. However, WFST necessitates a frame-by-frame search of CTC posterior probabilities through autoregression, which significantly hampers inference speed. In this work, we thoroughly investigate the spike property of CTC outputs and further propose the conjecture that adjacent frames to non-blank spikes carry semantic information beneficial to the model. Building on this, we propose the Spike Window Decoding algorithm, which greatly improves the inference speed by making the number of frames decoded in WFST linearly related to the number of spiking frames in the CTC output, while guaranteeing the recognition performance. Our method achieves SOTA recognition accuracy with significantly accelerates decoding speed, proven across both AISHELL-1 and large-scale In-House datasets, establishing a pioneering approach for integrating CTC output with WFST. © 2025 IEEE.

关键词： automatic speech recognition spike window decoding wfst

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Visualization of Hidden Patterns in WhatsApp Chat using Sentimental Analysis 3

Visualization of Hidden Patterns in WhatsApp Chat using Sent...

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3rd international conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Vinora, A. Suryakumar, L.S.S. Harivishnu, T.S. Jaspin, K. Selvaraj, Arikumar K. Nancy Deborah, M. Velammal College of Engineering and Technology Madurai India St. Joseph's Institute of Technology Chennai India SRM Institute of Science and Technology Chennai India

ISBN: (纸本)9798331527549

Challenges include handling unstructured chat data, ensuring privacy and ethical standards, managing large data volumes, accurately interpreting sentiment despite context, sarcasm, and irony, and creating effective visualizations. The proposed paper aims to develop a robust chat analyzer for WhatsApp conversations, leveraging Python libraries like pandas, matplotlib, seaborn, and natural language processing techniques. By filtering out irrelevant messages and group notifications, the tool focuses on meaningful interactions and employs sentiment analysis to provide insights into user dynamics. This comprehensive analyzer offers visualization and statistical analysis of chat data through a user-friendly application deployed on the Heroku web platform. The project improves comprehension of WhatsApp messages by integrating machine learning and NLP, allowing users to extract important data and handle unwanted content effectively. The suggested procedure involves utilizing a machine learning algorithm that combines VADER sentiment analysis for accurate emotion detection with keyword frequency analysis for trend identification. The new system shows a 15% boost in accuracy and a 20% increase in processing efficiency for sentiment analysis and user activity detection when compared to current models. © 2025 IEEE.

关键词： Data visualization matplotlib natural language processing (NLP) Python libraries (pandas seaborn) Sentiment analysis User behavior insights VADER WhatsApp chat analyzer

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An Enhanced and High-Performance Algorithm for Accurate Named Entity Recognition in Legal Text 1

An Enhanced and High-Performance Algorithm for Accurate Name...

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1st international conference on Sustainable Energy Technologies and Computational Intelligence, SETCOM 2025

作者： Naik, Varsha Rahalkar, Aniket Dr. Vishwanath Karad Mit World Peace University Department of Computer Engineering and Technology Pune India

ISBN: (纸本)9798331520540

The rise of digital technologies results in the generation of large volumes of legal data. Although natural language legal texts are easily understood by humans, they pose significant challenges for machine processing. Converting this information into a machine-readable format is essential for efficient legal data analysis. In this paper, we propose resource-efficient method for extracting and classifying named entities within legal documents. We use rule-based learning and regular expressions to identify legal entities like parties, courts, and case references. Once extracted, these entities are classified into categories such as individuals, legal organizations, and jurisdictions. To improve both accuracy and processing speed, we implement a modified K-Means algorithm clustering the extracted entities. This clustering step refines the classification by grouping entities based on their shared attributes. The proposed method significantly enhances the speed and precision of Named Entity Recognition (NER) for legal texts, enabling faster and more efficient legal data processing. © 2025 IEEE.

关键词： Unsupervised learning

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A Strategic Framework for the Development of a Microtext AI Chatbot for Tourism in Indian Cities 8

A Strategic Framework for the Development of a Microtext AI ...

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8th international conference on Electronics, Materials engineering and Nano-Technology, IEMENTech 2025

作者： Sultan, Adnan A. Mukherjee, Unmesh Mondal, Anupam Institute of Engineering & Management University of Engineering & Management Department of Computer Science & Engineering Kolkata India

ISBN: (纸本)9798331520762

Tourism plays a crucial role in India's economy, with its diverse cultural, historical, and geographical richness attracting millions of domes-tic and international visitors annually. To cater to the needs of these travelers and enhance their overall experience, there is a growing need for inno-vative technological solutions. This paper presents a comprehensive literature survey, analyzing existing research, trends, and practical implementations of microtext normalization as well as the application of chatbots in the context of tourism. Furthermore, it proposes a strategic framework for the development and deployment of an effective microtext AI chatbot tailored to the requirements of tourism in India. The proposed design leverages modern machine learning and natural language processing (NLP) strategies for providing personalized, context-aware assistance to tourists in order to enhance their overall experience while exploring the various cities of India. © 2025 IEEE.

关键词： Tourism

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Improve the Quality of Machine Translation in Low-Resource language Pairs 3rd

Improve the Quality of Machine Translation in Low-Resourc...

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3rd international conference on Advances in Information and Communication Technology, ICTA 2024

作者： Nguyen-Tien, Ha Nguyen-Ngoc, Phuc Nguyen-Van, Vinh Kim, Thai Dinh Hung Vuong University Phutho Viet Nam University of Engineering and Technology - VNU Hanoi Viet Nam International School Vietnam National University Hanoi100000 Viet Nam

ISBN: (纸本)9783031809422

Machine translation is a hard problem in natural language processing (NLP). Today, based on deep learning, the quality of machine translation systems is increasing strongly in rich-resource language pairs, but it is limited in low-resource language pairs, such as the Vietnamese-Laos language pair. In this work, we propose a method that depends on augmenting data and using a bilingual dictionary to improve the quality of machine translation for low-resource language pairs. Applying it to the Vietnamese-Laos language pair, it improved 14.0 BLEU scores for translating from Laos to Vietnamese and 3.5 BLEU scores for translating from Vietnamese to Laos. In addition, we built a good quality Vietnamese-Laos bilingual corpus that includes 182.208 sentence pairs to share with the community. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Machine translation

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Improved Sparse Upcycling for Instruction Tuning 31

Improved Sparse Upcycling for Instruction Tuning

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31st international conference on Computational Linguistics, COLING 2025

作者： Jiang, Wangyi Lu, Yaojie Lin, Hongyu Han, Xianpei Sun, Le Chinese Information Processing Laboratory Institute of Software Chinese Academy of Sciences China University of Chinese Academy of Sciences China

ISBN: (纸本)9798891761964

The Mixture-of-Experts (MoE) architecture has demonstrated significant potential in both large-scale pre-training and instruction tuning by offering increased parameter capacity without additional inference costs. However, developing MoE models faces challenges including training instability and the need for substantial high-quality training data. While efficient methodologies like sparse upcycling exist, they often lead to performance degradation in instruction tuning scenarios. We introduce representation-based sparse upcycling, a straightforward yet effective technique for converting dense language models into sparsely activated ones while maintaining similar computational costs. Unlike conventional sparse upcycling, our approach leverages intermediate representations from language models to initialize router weights. This strategy addresses the mismatch between randomly initialized and well-trained parameters while providing prior knowledge to guide expert specialization during training. Extensive experiments across diverse benchmarks demonstrate significant improvements in both model capabilities and routing consistency compared to existing approaches. © 2025 Association for Computational Linguistics.

关键词： Computational linguistics

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Error Bounds Revisited, and How to Use Bayesian Statistics While Remaining a Frequentist

Error Bounds Revisited, and How to Use Bayesian Statistics W...

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2025 IEEE international conference on Acoustics, Speech, and Signal processing, ICASSP 2025

作者： Xu, Ning Foster, Christopher M. Manton, Jonathan H. School of Internet of Things Engineering Jiangnan University Wuxi214122 China Department of Electrical and Electronic Engineering The University of Melbourne Parkville Australia

ISBN: (纸本)9798350368741

Signal processing makes extensive use of point estimators and accompanying error bounds. These work well up until the likelihood function has two or more high peaks. When it is important for an estimator to remain reliable, it becomes necessary to consider alternatives, such as set estimators. An obvious first choice might be confidence intervals or confidence regions, but there can be difficulties in computing and interpreting them (and sometimes they might still be blind to multiple peaks in the likelihood). Bayesians seize on this to argue for replacing confidence regions with credible regions. Yet Bayesian statistics require a prior, which is not always a natural part of the problem formulation. This paper demonstrates how a reinterpretation of the prior as a weighting function makes an otherwise Bayesian estimator meaningful in the frequentist context. The weighting function interpretation also serves as a reminder that an estimator should always be designed in the context of its intended application;unlike a prior which ostensibly depends on prior knowledge, a weighting function depends on the intended application. This paper uses the time-of-arrival (TOA) problem to illustrate all these points. It also derives a basic theory of region-based estimators distinct from confidence regions. © 2025 IEEE.

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