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KACE: Generating knowledge-Aware Contrastive Explanations for natural language Inference 59

KACE: Generating Knowledge-Aware Contrastive Explanations fo...

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Joint conference of 59th Annual Meeting of the Association-for-Computational-Linguistics (ACL) / 11th international Joint conference on natural language processing (IJCNLP) / 6th Workshop on Representation Learning for NLP (RepL4NLP)

作者： Chen, Qianglong Ji, Feng Zeng, Xiangji Li, Feng-Lin Zhang, Ji Chen, Haiqing Zhang, Yin Zhejiang Univ Coliege Comp Sci & Technol Hangzhou Peoples R China Alibaba Grp DAMO Acad Hangzhou Peoples R China Tencent Shenzhen Peoples R China Alibaba Grp Hangzhou Peoples R China

ISBN: (纸本)9781954085527

In order to better understand the reason behind model behaviors (i.e., making predictions), most recent work has exploited generative models to provide complementary explanations. However, existing approaches in natural language processing (NLP) mainly focus on "WHY A" rather than contrastive "WHY A NOT B", which is shown to be able to better distinguish confusing candidates and improve model performance in other research fields. In this paper, we focus on generating Contrastive Explanations with counterfactual examples in NLI and propose a novel knowledge-Aware generation framework (KACE). Specifically, we first identify rationales (i.e., key phrases) from input sentences, and use them as key perturbations for generating counterfactual examples. After obtaining qualified counterfactual examples, we take them along with original examples and external knowledge as input, and employ a knowledge-aware generative pre-trained language model to generate contrastive explanations. Experimental results show that contrastive explanations are beneficial to clarify the difference between predicted answer and other answer options. Moreover, we train an BERT-large based NLI model enhanced with contrastive explanations and achieve an accuracy of 91.9% on SNLI, gaining an improvement of 5.7% against ETPA ("Explain-Then-Predict-Attention") and 0.6% against NILE ("WHY A").

关键词： natural language processing systems

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ByteScience: Bridging Unstructured Scientific Literature and Structured Data with Auto Fine-tuned Large language Model in Token Granularity

ByteScience: Bridging Unstructured Scientific Literature and...

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IEEE international conference on Data Mining Workshops (ICDM Workshops)

作者： Tong Xie Hanzhi Zhang Shaozhou Wang Yuwei Wan Imran Razzak Chunyu Kit Wenjie Zhang Bram Hoex GreenDynamics Australia School of Photovoltaic and Renewable Energy Engineering University of New South Wales Australia Dept. of Computer Science and Engineering University of North Texas Denton United States School of Computer Science and Engineering University of New South Wales Australia Dept. of Linguistics and Translation City University of Hong Kong Hong Kong China

ISBN: (数字)9798331530631

ISBN: (纸本)9798331530648

natural language processing (NLP) is widely used to supply summarization ability from long context to structured information. However, extracting structured knowledge from scientific text by NLP models remains a challenge because of its domain-specific nature to complex data preprocessing and the granularity of multi-layered device-level information. To address this, we introduce ByteScience, a non-profit cloud-based auto finetuned Large language Model (LLM) platform, which is designed to extract structured scientific data and synthesize new scientific knowledge from vast scientific corpora. The platform capitalizes on DARWIN, an open-source, fine-tuned LLM dedicated to natural science. The platform was built on Amazon Web Services (AWS) and provides an automated, user-friendly workflow for custom model development and data extraction. The platform achieves remarkable accuracy with only a small amount of well-annotated articles. This innovative tool streamlines the transition from the science literature to structured knowledge and data and benefits the advancements in natural informatics. Demo Video

关键词： Cloud computing Accuracy Web services Large language models Computational modeling Propulsion natural language processing Data models Data mining Informatics

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LiberationKG: A knowledge Graph of Bangladesh Liberation War from Newspaper Articles

LiberationKG: A Knowledge Graph of Bangladesh Liberation War...

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international conference on Computer and Information Technology (ICCIT)

作者： Mohammad Fahim Foisal Rudra Pratap Deb Nath Nasrin Sultana Dept. of Computer Science and Engineering Big Data Information and Knowledge Engineering (BIKE) Lab University of Chittagong Bangladesh

ISBN: (数字)9798331519094

ISBN: (纸本)9798331519100

The Bangladesh Liberation War of 1971 remains one of the most significant and emotional events in the country’s history. In today’s digital world, historical narratives are at risk of being distorted by disinformation and the spread of false news. Newspaper articles from unbiased sources provide a reliable overview of the situation. However, while unstructured texts are easily understandable for humans, they are not readily interpretable by machines. To address this, we present Libera-tionKG, a knowledge graph of the Bangladesh Liberation War, generated from reputable sources such as Time Magazine and The New York Times. LiberationKG is created by producing a total of 12,291 Resource Description Framework (RDF) triples from the mentioned sources using a Large language Model and constructs from RDFS, OWL, and *** vocabularies. Its development involves advanced natural language processing techniques, RDF triple validation and optimization, and linking to external resources. LiberationKG provides an organized representation of knowledge and an efficient system for information extraction. With 80% accuracy, LiberationKG is linked to 521 external resources and adheres to FAIR principles, ensuring interoperability and data accessibility, making it a valuable digital resource for preserving this pivotal historical event.

关键词： Vocabulary Large language models Pipelines OWL knowledge graphs Resource description framework natural language processing Reliability Optimization Interoperability

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Intelligent Question Answering System Based on Cognitive Graph: A Case Study of Textile Fabrics 23

Intelligent Question Answering System Based on Cognitive Gra...

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23rd IEEE international conference on Software Quality, Reliability, and Security Companion, QRS-C 2023

作者： He, Xinyu Cui, Mengyao Chen, Yufan Liu, Longfei Wang, Bangchao School of Computer Science and Artificial Intelligence Wuhan Textile University China Wuhan Textile University Engineering Research Center of Hubei Province for Clothing Information China

ISBN: (纸本)9798350359398

With the continuous development of artificial intelligence technology, more and more natural language processing technologies are applied to the textile industry. How to extract information from massive textile data and give more targeted answers to users' questions is a vital issue in promoting textile intelligence. Therefore, taking textile fabrics as an example, this paper proposes an intelligent question-Answering system based on cognitive graphs. Based on the knowledge of textile fabrics as the knowledge source, a cognitive graph question-Answering system is constructed. Firstly, the Bidirectional Encoder Representation from Transformers-Bi-directional Long Short-Term Memory-Conditional Random Fields (BERT+BiLSTM+CRF) model is applied to perform entity recognition on the question sentence. Subsequently, node traversal is performed utilizing Cypher statements. Following that, answer reasoning is conducted employing the Graph Convolutional Network (GCN) algorithm, and the obtained answer is implemented as the output result. Finally, the system undergoes functional testing to verify its capability to accurately return the question's answer. The empirical results indicate the feasibility of the proposed cognitive graph-based question-Answering system and its specific generalization and scalability. © 2023 IEEE.

关键词： Textile industry

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natural language processing Techniques for Enhancing Automated Essay Scoring Systems

Natural Language Processing Techniques for Enhancing Automat...

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international conference on Computational and Characterization Techniques in engineering & Sciences (CCTES)

作者： M. Vinoth Kumar Vichitra Sivaji B V V Padmavathi N. Jothi B. Muthulakshmi V R. Yasu Bharathi Department of English Vel Tech Rangarajan Dr Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu India Department of English Sri Ramakrishna Engineering College Coimbatore Tamil Nadu Siddhartha School of Engineering Siddhartha Academy of Higher Education Vijayawada Geetha Jeevan Arts and Science College Thoothukudi Tamilnadu India Department of English Manonmaniam Sundaranar University Tirunelveli Tamilnadu India

ISBN: (数字)9798350364699

ISBN: (纸本)9798350364705

In this experimental work, we explore how Automated Essay Scoring (AES) systems can be improved through the use of sophisticated natural language processing (NLP) approaches. Discretion and impartiality are becoming more and more important, especially when educational institutions are depending more and more on automatic technologies to assess essays. This study examines the most recent advances in natural language processing (NLP), including syntactic parsing, deep literacy algorithms, and semantic analysis, and assesses their applicability to the ongoing refinement of AES systems. The research specifically focuses on evaluating the utility of various approaches. This work aims to address issues related to environment knowledge, consonance evaluation, and bias reduction in automated scoring using these techniques. These techniques will be integrated to achieve this. The results show that the application of sophisticated NLP techniques may produce new, valid, and trustworthy assessment problems. This could be one of the main causes of the widespread AES abandonment in educational environments. This work opens the possibility that natural language processing (NLP) could be used to change the essay score by offering a more thorough and nuanced assessment of student notes. This would have the effect of improving every facet of the teaching process.

关键词： Accuracy Semantics Refining Education Pressing Syntactics natural language processing Reliability

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A Comprehensive Study on Advancements in Text Mining and natural language processing

A Comprehensive Study on Advancements in Text Mining and Nat...

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Emerging Research in Computational Science (ICERCS), international conference on

作者： Sarthak Mahire U K Balaji Saravanan Murali M Prabakaran N School of Computer Science and Engineering Vellore Institute of Technology Vellore India Department of Computer Science and Engineering Knowledge Institute of Technology Salem India Department of Information Technology Sona College of Technology Salem India

ISBN: (数字)9798331534967

ISBN: (纸本)9798331534974

Text mining and natural language processing (NLP) have witnessed significant advancements in recent years, driven by the increasing availability of unstructured data and the development of sophisticated machine learning models. This review explores the evolution of text mining and NLP, highlighting the transition from rule-based systems to modern deep learning approaches. This paper reviews various techniques and its impact across various domains. Challenges such as data bias and model interpretability still remain. The paper also discusses future directions, emphasizing the need for fair, interpretable and sustainable NLP systems. This paper aims to provide insights into the current state of text mining and NLP, the challenges faced and potential pathways for future research.

关键词： Text mining Training Technological innovation Reviews Scientific computing Focusing Medical services natural language processing Data models Multilingual

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Research on compression pruning methods based on deep learning 3

Research on compression pruning methods based on deep learni...

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3rd international conference on Signal processing and Machine Learning, CONF-SPML 2023

作者： Cai, Mingzhuo Su, Yihong Wang, Binyu Zhang, Tianyu College of Integrated Circuit Science and Engineering Nanjing University of Posts and Telecommunications Nanjing China Haide College Ocean University of China Qingdao China DGUT-CNAM Institute Dongguan University of Technology Dongguan China Faculty of Liberal Arts and Social Sciences Hong Kong University of Education Hong Kong Hong Kong

In the past decade or so, deep neural networks have continuously refreshed the best performance in tasks such as computer vision and natural language processing, and have become the most concerned research directions. Although deep neural networks have significant performance, they are difficult to deploy on hardware-constrained embedded and mobile devices due to their huge number of parameters, computational and storage costs. Therefore, in recent years, the lightweight design of network structure has gradually become a cutting-edge and popular direction, improving the operation speed and optimizing the storage space under the premise of maintaining the accuracy of the neural network. In this paper, the results achieved by domestic and foreign scholars in deep learning model compression are sorted and classified, and the methods of network pruning, model quantification, knowledge distillation, and lightweight network design are summarized, and the compression effect of related methods on known public models is summarized. Finally, the possible directions, application directions and development trends of future neural network model lightweight research are prospected. © Published under licence by IOP Publishing Ltd.

关键词： Deep neural networks

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Applying Deep Learning Strategies for Optimizing Real-Time Data Analysis in Machine Learning 3

Applying Deep Learning Strategies for Optimizing Real-Time D...

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3rd international conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Mishra, Varun Dev, Sudhanshu Singh, Akhilendra Pratap Krishna, Gautham Geetha, B. Kadam, Rutuja S. Vivekananda Global University Department of Computer Science & Engineering Jaipur India Chitkara University Institute of Engineering and Technology Chitkara University Centre of Interdisciplinary Research in Business and Technology Punjab India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Faculty of Engineering and Technology Department of Civil Engineering Karnataka Ramnagar562112 India Prince Shri Venkateshwara Padmavathy Engineering College Department of Science and Humanities Chennai127 India Pune India

ISBN: (纸本)9798350319125

Deep mastering is becoming increasingly essential for fact analysis in machine learning. It has been used to remedy various applications, from natural language processing to photograph recognition, and has made goodsized achievements in many fields. In this paper, we discover a way to use deep getting-to-know for actual-time data evaluation optimization in gadget learning. Mainly, we present a deep getting-to-know framework for real-time statistics analysis optimization, including an expansion of deep gaining knowledge of fashions and techniques. We speak about the capability of actual-time statistics evaluation optimization with deep getting-to-know and numerous improvement techniques for making the optimization method extra green. Eventually, we offer an empirical evaluation of the proposed framework in machine studying packages, demonstrating the usefulness of presenting a deep learning-based totally technique to real-time statistics analysis optimization. We finish the paper with a dialogue of destiny directions for deep learning research for actual-time information evaluation optimization. © 2023 IEEE.

关键词： Deep learning

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Learning Semantic Textual Similarity via Topic-informed Discrete Latent Variables

Learning Semantic Textual Similarity via Topic-informed Disc...

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2022 conference on Empirical Methods in natural language processing, EMNLP 2022

作者： Yu, Erxin Du, Lan Jin, Yuan Wei, Zhepei Chang, Yi School of Artificial Intelligence Jilin University China Key Laboratory of Symbolic Computation and Knowledge Engineering Jilin University China International Center of Future Science Jilin University China Faculty of Information Technology Monash University Australia

Recently, discrete latent variable models have received a surge of interest in both natural language processing (NLP) and Computer Vision (CV), attributed to their comparable performance to the continuous counterparts in representation learning, while being more interpretable in their predictions. In this paper, we develop a topic-informed discrete latent variable model for semantic textual similarity, which learns a shared latent space for sentence-pair representation via vector quantization. Compared with previous models limited to local semantic contexts, our model can explore richer semantic information via topic modeling. We further boost the performance of semantic similarity by injecting the quantized representation into a transformer-based language model with a well-designed semantic-driven attention mechanism. We demonstrate, through extensive experiments across various English language datasets, that our model is able to surpass several strong neural baselines in semantic textual similarity tasks. © 2022 Association for Computational Linguistics.

关键词： Semantics

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Personalized Academic Communication Recommendation System Based on knowledge Graph

Personalized Academic Communication Recommendation System Ba...

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Electronic Communication and Artificial Intelligence (IWECAI), international Workshop on

作者： Jiajia Liu Meilin Luan Ning Jiang School of Information Engineering Wuhan University of Technology Wuhan China International Office Wuhan University of Technology Wuhan China

ISBN: (数字)9798350386943

ISBN: (纸本)9798350386950

Scholar retrieval resources are faced with the problems of information overload, low efficiency, and low matching degree. To address this issue, we developed a personalized academic resource recommendation system based on the knowledge graph. The system consists of a data layer, a policy layer, and an application layer, which include four main functional modules: resource storage module, knowledge description module, wisdom recommendation module, and user management module. The back-end of the system utilizes the Spring Boot, Spring MVC, and MyBatis frameworks, while the front-end communicates with the back end via HTTP requests. The system database primarily utilizes MySQL and Neo4j. The system integrates knowledge graph and natural language processing technology to recommend high-quality academic resources for scholars.

关键词： Databases Collaborative filtering knowledge graphs natural language processing HTTP Springs Artificial intelligence

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