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检索条件"任意字段=Conference on empirical methods in natural language processing"

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An empirical Study of Backdoor Attacks on Masked Auto Encoders 48

An Empirical Study of Backdoor Attacks on Masked Auto Encode...

引用

48th IEEE International conference on Acoustics, Speech and Signal processing, ICASSP 2023

作者： Zhuang, Shuli Xia, Pengfei Li, Bin University of Science and Technology of China Hefei China

ISBN: (纸本)9781728163277

Large-scale unlabeled data has spurred recent progress in self-supervised learning methods for learning rich visual representations. Masked autoencoders (MAE), a recently proposed self-supervised method, has exhibited exemplary performance on many vision tasks by masking and reconstructing random patches of input. However, as a representation learning method, the backdoor pitfall of MAE, and its impact on downstream tasks, have not been fully investigated. In this paper, we use several common triggers to perform backdoor attacks on the pre-training phase of MAE and test them on downstream tasks. We explore some key factors such as trigger patterns and the number of poisoned samples. Some interesting results can be obtained. The pre-training process of MAE can be used to enhance the memory of the encoder for the trigger mode. The global trigger is easier than local triggers to attack the encoder. The blended ratio and patch size of the triggers have a great impact on MAE. Code is available at https://***/zhuangshuli/MAE-Backdoor. © 2023 IEEE.

关键词： Backdoor attack Data poisoning Masked autoencoders

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Addressing the Libyan Arabic Dialect Identification: A Comparative Study of Ensemble Classification methods 4

Addressing the Libyan Arabic Dialect Identification: A Compa...

引用

4th IEEE International Maghreb Meeting of the conference on Sciences and Techniques of Automatic Control and Computer Engineering, MI-STA 2024

作者： Ghumeid, Najah A. Essgaer, Mansour Sebha University Faculty of Sciences Computer Science Department Sebha Libya Sebha University Faculty of Information Technology Information Systems Department Sebha Libya

ISBN: (纸本)9798350372632

Dialect identification presents a challenging task within the realm of natural language processing. The growing utilization of Arabic dialects in written form, particularly on social media platforms, has created new demands for accurate identification of these dialects. In multilingual datasets, distinguishing the Libyan Arabic dialect from neighboring Arabic dialects has emerged as a crucial goal. To address this, we conducted experiments using different lengths of word n-gram features to determine the most suitable combination of word n-grams and term frequency-inverse document frequency. Our aim was to capture contextual information effectively while mitigating linguistic similarities between Arabic dialects of neighboring countries. The QCRI Arabic Dialects Identification dataset was employed, and we evaluated the performance of three ensemble tree-based classification methods: Random Forest, Gradient Boost Machine, and eXtreme Gradient Boosting. The evaluation, based on the f-measure metric, yielded respective scores of 68%, 37%, and 52% for Random Forest, Gradient Boost Machine, and eXtreme Gradient Boosting. These results underscore the effectiveness of our approach in identifying the Libyan Arabic dialect and highlight the potential of ensemble tree-based methods in Arabic dialect identification tasks. © 2024 IEEE.

关键词： Computational linguistics

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Neural Machine Translation: A Survey of methods used for Low Resource languages 10

Neural Machine Translation: A Survey of Methods used for Low...

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17th INDIACom;2023 10th International conference on Computing for Sustainable Global Development, INDIACom 2023

作者： Qumar, Syed Matla Ul Azim, Muzaffar Quadri, S.M.K. Jamia Millia Islamia Department of Computer Science New Delhi India

ISBN: (纸本)9789380544472

Machine translation (MT) is a maj or natural language processing subfield that is designed to automatically translate human spoken languages. Neural machine translation (NMT) has gained significant achievement in recent times and is becoming the emerging dominant approach in actual MT systems. The availability of significant quantities of parallel data is critical for the performance of Neural Machine Translation (NMT) models. A key challenge in machine translation for many language pairs is a dearth of parallel corpora. An important research concern in the field of machine translation is enhancing NMT accuracy for languages with limited resources. Extensive research is being done to enhance Neural Machine Translation accuracy by leveraging a limited parallel dataset. In this paper, we try to provide a broad and well-structured review of the methods used to enhance the translation accuracy of low-resource languages by exploiting monolingual data, which is typically easy to get and is available in a considerably higher amount, and further classification in two broad categories according to the adjustments they required to the NMT model's architecture is done. We believe that our survey will be useful to new students starting in this field. © 2023 Bharati Vidyapeeth, New Delhi.

关键词： Neural machine translation

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"Study on the Grammatical Function of "la dhon"as Function Words in the Tibetan language processing" 23

"Study on the Grammatical Function of "la dhon"as Function W...

引用

Proceedings of the 2023 6th International conference on Machine Learning and natural language processing

作者： Cairang Suonan Duojie Gongque Qinghai Normal University China

ISBN: (纸本)9798400709241

The study of Tibetan function words is an indispensable basic work in Tibetan natural language processing and has a wide range of practical application value. It is the core of Tibetan information processing and the basis of Tibetan natural language understanding, and has wide application prospects in Tibetan proofreading, information retrieval, bilingual translation, automatic classification and other technologies. On the basis of previous studies, this paper discusses the grammatical functions of Tibetan function words la dhon from the perspective of information processing, classification system and formal description of unfree function words, focuses on the grammatical functions of Tibetan function words la dhon, and puts forward the preliminary conception and processing methods of Tibetan function words.

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A Novel LLM-Powered Enterprise-Specific Conversational Search Framework

A Novel LLM-Powered Enterprise-Specific Conversational Searc...

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Machine Learning and natural language processing (MLNLP), International conference on

作者： Zhenyu Liu Lan Ma Zhigang Wang Runze Nie Ping Zhang Department of Artificial Intelligence Technology Aisino Co.Ltd. Beijing China

ISBN: (数字)9798350354973

ISBN: (纸本)9798350354980

Organizations are increasingly adopting chatbots to deliver enterprise-specific search services, providing conversational and contextually appropriate natural language responses to user queries. To meet complex demands and enhance operational efficiency, while overcoming the limitations of traditional retrieval methods, we present an advanced approach for developing a Large language Model (LLM)-Powered Enterprise Conversational Search Framework (LECS). This framework employs a sophisticated mixedgranularity chunking strategy for processing both short and long texts, an advanced question-rewriting mechanism, and a comprehensive multi-source ranking system. Extensive experiments have demonstrated the superior performance of LECS, particularly in terms of recall@5 and answer accuracy.

关键词： Accuracy Large language models Organizations Machine learning Chatbots

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NaijaSenti: A Nigerian Twitter Sentiment Corpus for Multilingual Sentiment Analysis 13

NaijaSenti: A Nigerian Twitter Sentiment Corpus for Multilin...

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13th International conference on language Resources and Evaluation (LREC)

作者： Muhammad, Shamsuddeen Hassan Adelani, David Ifeoluwa Ruder, Sebastian Ahmad, Ibrahim Sa'id Abdulmumin, Idris Bello, Bello Shehu Choudhury, Monojit Emezue, Chris Chinenye Abdullahi, Saheed Salahudeen Aremu, Anuoluwapo Jorge, Alipio Brazdil, Pavel LIAAD INESC TEC Porto Portugal Univ Porto Fac Sci Porto Portugal Saarland Univ Spoken Language Syst Grp LSV Saarbrucken Germany Google Res Mountain View CA USA Bayero Univ Fac Comp Sci & Informat Technol Kano Nigeria Ahmadu Bello Univ Dept Comp Sci Zaria Nigeria Microsoft Res India Bengaluru India Tech Univ Munich Munich Germany Clear Global Portland OR USA Kaduna State Univ Nasarawa Nigeria Masakhane NLP Johannesburg South Africa HausaNLP Kano Nigeria

ISBN: (纸本)9791095546726

Sentiment analysis is one of the most widely studied applications in NLP, but most work focuses on languages with large amounts of data. We introduce the first large-scale human-annotated Twitter sentiment dataset for the four most widely spoken languages in Nigeria-Hausa, Igbo, Nigerian-Pidgin, and Yoruba-consisting of around 30,000 annotated tweets per language, including a significant fraction of code-mixed tweets. We propose text collection, filtering, processing, and labeling methods that enable us to create datasets for these low-resource languages. We evaluate a range of pre-trained models and transfer strategies on the dataset. We find that language-specific models and language-adaptive fine-tuning generally perform best. We release the datasets, trained models, sentiment lexicons, and code to incentivize research on sentiment analysis in under-represented languages.

关键词： sentiment analysis low-resource twitter corpus natural language processing

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Comprehensive study of Sign language Conversion Using Machine Learning 10

Comprehensive study of Sign Language Conversion Using Machin...

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10th IEEE Uttar Pradesh Section International conference on Electrical, Electronics and Computer Engineering, UPCON 2023

作者： Sahu, Santhoshini Palisetti, Sai Geetha Peddinti, Harshavardhini Yalamati, Sandeep Chakravarthi Vajja, PremSai Rushyasrungu, Greeshmika Andhra Pradesh India

ISBN: (纸本)9798350382471

In this world, communication plays a major role to interact with each other. It acts like a bridge to convey message to other people. Human beings are able to manage their tasks by making use of their five senses, in which hearing is strongest among them. Some people have hearing loss from birth which results in physical health issues, which makes it difficult for them to acquire and say new words. These kinds of physically challenged people are known as Deaf and Dumb. The people who have hearing or speech impairments use the sign language to convey their messages. A Sign language is a combination of the hand gestures, face emotions and body movements. By using the sign language deaf and dumb people can communicate and deliver their message to others. There are several different varieties of sign language, primarily American Sign language and British Sign language. Normal individuals, however, struggle to understand sign language and the significance of others' actions since they are unable to use it. Our motivation for this study is to know how normal people can easily communicate with people deaf and dumb who understands only sign language. Here we analyses of various papers and their methods for solving this problem are discussed. The main solution of this situation is to create models which are helpful to understand the meaning behind the actions. So many models are created with various approaches like SURF with SVM and CNN, Neural machine translation, Deep LSTM, natural language processing and other techniques. This study is done across various approaches to know the performance of each model their advantages and disadvantages in detail. © 2023 IEEE.

关键词： CNN Deep Learning Frameworks LSTM NLP Sign language

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Blind Image Super-Resolution via Domain Translation and Diffusion Models

Blind Image Super-Resolution via Domain Translation and Diff...

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natural language processing (ICNLP), International conference on

作者： Ying Zhang Yinghua Li Zifei Zhang Chaojun Zhang Jinglu He Center for Image and Information Processing Xi’an University of Posts and Telecommunications Xi’an China Research and Development Department Southwest Technology and Engineering Research Institute Chongqing China

ISBN: (数字)9798350349115

ISBN: (纸本)9798350349122

Blind image super-resolution is gaining increasing attention due to its significant practical implications. Poor model generalization results from the assumption that low-resolution (LR) images are the consequence of bicubic downsampling of high-resolution (HR) images, which is made by a number of existing super-resolution methods. We employ a degradation model similar to BSRGAN to synthesize LR images, making them appear more realistic, and then treat the blind image super-resolution problem as a domain translation issue, utilizing diffusion models for resolution enhancement.

关键词： Degradation Superresolution Diffusion models natural language processing Image reconstruction

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Continuous Prompt for Chemical language Model Aided Anticancer Synergistic Drug Combination Prediction

Continuous Prompt for Chemical Language Model Aided Anticanc...

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2023 IEEE International conference on Big Data, BigData 2023

作者： Geng, Guannan Zhao, Lingling Wang, Chunyu Liu, Xiaoyan Wang, Junjie The First Affiliated Hospital of Harbin Medical University Department of Endocrinology Harbin China Harbin Institute of Technology Faculty of Computing Harbin China Nanjing Medical University School of Biomedical Engineering and Informatics Department of Medical Informatics Nanjing China

ISBN: (纸本)9798350324457

Identifying synergistic drug combinations is paramount significance in addressing complex diseases while reducing the risks of toxicities and other adverse effects. Although a plethora of computational methods have been proposed in this domain, most of them are underpinned only by physicochemical or biological features. Recently, Chemical language Models (CLMs) are shown to be capable of learning better representations that hold utility across diverse tasks, from molecular property prediction, de novo drug design, drug-target interaction, and more. In this study, we proposed CLMSyn, a continuous prompt for CLM aided synergistic drug combinations prediction. Unlike existing works employ CLMs for downstream tasks, we adopt the prompt learning to fine-tune CLM, that is, only train small-scale prompt while keeping CLM fixed. Furthermore, we harness the the multi-head attention mechanism to fuse the learned vector from the CLM, chemical descriptors and gene expression of cell line. A comprehensive array of experiments conducted on a benchmark dataset, encompassing four distinct synergy types, substantiates the superior performance of CLMSyn when contrasted against existing state-of-the-art methods. These empirical findings provide compelling evidence attesting to the efficacy of CLMSyn as a potent instrumentality in expediting the identification of pioneering combination therapies. © 2023 IEEE.

关键词： Chemical language Model Drug Combination Multi-head Attention Prompt Learning

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Exploring the role of transformer-based language models in medical transcript summarization 7

Exploring the role of transformer-based language models in m...

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7th International conference on Informatics and Data-Driven Medicine, IDDM 2024

作者： Zimokha, Mariia Yemets, Kyrylo Queen Mary University of London London United Kingdom Lviv Polytechnic National University Lviv Ukraine

The increasing complexity of medical documentation necessitates effective summarization tools that enable healthcare professionals to swiftly access critical patient information. This study investigates the efficacy of four transformer-based models-T5, Flan-T5, GPT-4 Mini, and BART-in summarizing medical transcripts. We conducted a comprehensive comparative analysis utilizing various learning paradigms, including zero-shot learning, one-shot learning, and pretraining methods, to assess the models' capabilities. For evaluation, we employed a diverse set of metrics: ROUGE, METEOR, and BERTScore, providing a holistic view of each model's performance. Our findings indicate that the BART model consistently outperformed the others in summarizing medical texts, demonstrating superior fluency and coherence. In contrast, the GPT-4 Mini model exhibited notable flexibility in domain-specific fine-tuning through zero-shot learning. These results highlight the potential of leveraging transformer-based models to enhance the efficiency of medical documentation processes, ultimately contributing to improved patient care and clinical outcomes. This study underscores the importance of integrating advanced natural language processing techniques into healthcare practices to address the challenges posed by complex medical information. © 2024 CEUR-WS. All rights reserved.

关键词： Zero-shot learning

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