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A Dataset and Baselines for Multilingual Reply Suggestion 59

A Dataset and Baselines for Multilingual Reply Suggestion

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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： Zhang, Mozhi Wang, Wei Deb, Budhaditya Zheng, Guoqing Shokouhi, Milad Awadallah, Ahmed Hassan Univ Maryland College Pk MD 20742 USA Qualtrics Provo UT USA Microsoft AI Redmond WA USA Microsoft Res Redmond WA USA

ISBN: (纸本)9781954085527

Reply suggestion models help users process emails and chats faster. Previous work only studies English reply suggestion. Instead, we present MRS, a multilingual reply suggestion dataset with ten languages. MRS can be used to compare two families of models: 1) retrieval models that select the reply from a fixed set and 2) generation models that produce the reply from scratch. therefore, MRS complements existing cross-lingual generalization benchmarks that focus on classification and sequence labeling tasks. We build a generation model and a retrieval model as baselines for MRS. the two models have different strengths in the monolingual setting, and they require different strategies to generalize across languages. MRS is publicly available at https://***/zhangmozhi/mrs.

关键词： Classification (of information)

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EMERGING thREAT of DEEP FAKE: HOW to IDENTIFY and PREVENT IT 6

EMERGING THREAT of DEEP FAKE: HOW to IDENTIFY and PREVENT IT

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6th international conference on Future Networks and Distributed Systems, ICFNDS 2022

作者： Taha, Murooj Amer Khudhair, Wijdan Mahood Khudhur, Ahmed Mahmood Mahmood, Omar Abdulkareem Hammadi, Yousif I. Al-Husseinawi, Riyam Shihab Ahmed Aziz, Ahmed Bilad Alrafidain University College Department of Computer Engineering Techniques Diyala Iraq Department of Communications Engineering College of Engineering University of Diyala 32001 Iraq Bilad Alrafidain University College Department of Medical Instruments Engineering Techniques Diyala Iraq International Business Management Department Tashkent State University of Economics Tashkent Uzbekistan

ISBN: (纸本)9781450399050

Although manipulations of visual and aural media have been around for as long as there have been media, the relatively recent introduction of deep fakes has marked a turning point in the creation of fake information. Deep fakes are automated methods that allow the creation of fake information that is becoming increasingly difficult for human observers to see. these procedures are made possible by the most recent technological advancements in artificial intelligence and deep learning. Deep Learning is a powerful method that is now being implemented in a variety of industries, including natural language processing, computer vision, image processing, and machine vision. Deep fakes are created by the use of deep learning algorithms in order to synthesize and modify photos, videos, or sounds of a person, to the point that human people are unable to discern the fake from the real one. In this article, we present a workable description of deep fakes as well as an outline of the technology that lies underneath them. In order to assist people in thinking about the future of deep fakes, we outline the benefits of the deepfake as well as the potential threats. this study presents a complete analysis of deep fake methods and discusses the most effective strategies for preventing counterfeiting. © 2022 ACM.

关键词： Computer vision

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MSFF-FCOS: An object detector based on improved FCOS for UAV aerial images

MSFF-FCOS: An object detector based on improved FCOS for UAV...

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Natural language processing (ICNLP), international conference on

作者： Sidi Xu Dianwei Wang Jie Fang Yuanqing Li Zhijie Xu School of Communications and Information Engineering Xi'an University of Posts and Telecommunications Xi'an China School of Computing and Engineering University of Huddersfield Huddersfield UK

ISBN: (数字)9798350349115

ISBN: (纸本)9798350349122

Objects detection in unmanned aerial vehicles (UAVs) imagery plays an important role in environmental monitoring, post-disaster rescue and other fields. However, detecting objects in aerial images is a challenging task due to the density of small objects with little detailed information. To address these issues, we propose a new object detection method based on improved FCOS with multi-scale features feature interaction for UAV aerial images (MSFF-FCOS). Firstly, an innovative feature extraction module is proposed to preserve the effective information of small objects through Contextual Transformer module (CoTM), and its capability to enhance the feature representation of the small objects. And then, a new feature fusion network is designed which incorporate parallel dilated convolution modules to get multi-scale contextual information. Finally, the Dual Weighting Label Assignment (DWLA) method has be used to improve the positional accuracy of small targets in UAV aerial images and can reduce missed detections. the experimental results show that the average precision (AP) of the proposed method reaches 24.0%, which is 3.4% higher than that of the original FCOS method in the VisDrone2019 dataset. Furthermore, the method has a better generalization performance on our self-picked dataset.

关键词： Accuracy Convolution Object detection Detectors Autonomous aerial vehicles Feature extraction Transformers Cameras Natural language processing Environmental monitoring

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Deep-Learning-Based Automated Scoring for the Severity of Toxic Comments Using Electra 22

Deep-Learning-Based Automated Scoring for the Severity of To...

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6th international conference on Deep Learning Technologies, ICDLT 2022

作者： Zhang, Tiancong Department of Computer Science and Technology Dalian Neusoft University of Information Dalian116023 China

ISBN: (纸本)9781450396936

With the increasing popularity of the Internet, social media plays a crucial role in people's daily communication. However, due to the anonymity of Internet, toxic comments emerge in an endless stream on the Internet, which seriously affects the health of online social environment. To effectively reduce the impact of toxic comments, automated scoring methods for the severity of toxic comments are in great demand. For that purpose, a deep-learning-based natural language processing technique is proposed using ELECTRA to automatically score the toxicity of a comment in this work. the backbone of our model is the ELECTRA discriminator, and the downstream regression task is accomplished by the following head layer. three head layers are implemented separately: multi-layer perceptron, convolutional neural network, and attention. the dataset used for model training is from the Kaggle competition Toxic Comment Classification Challenge, and the model performance is evaluated through another Kaggle competition Jigsaw Rate Severity of Toxic Comments. By a boost from the K-Fold cross validation and an ensemble of three models with different head layers, our method can reach a competition score 0.80343. Such score ranks 71/2301 (top 3.1%) in the leaderboard and can get a silver medal in the competition. the results in this work would help filter the toxic comments and harmful text information automatically and effectively on the Internet, and could greatly reduce the cost of manual review and help build a healthier Internet environment. © 2022 ACM.

关键词： Multilayer neural networks

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Transformer-Based Approaches for Research Paper Categorization

Transformer-Based Approaches for Research Paper Categorizati...

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Mobile Computing and Sustainable Informatics (ICMCSI), international conference on

作者： Anguluri Venkata Siva Pavani Sujitha M. Suneetha Mohammad Shameera Department of Information Technology Velagapudi Ramakrishna Siddhartha Engineering College Vijayawada Andhra Pradesh India

ISBN: (数字)9798331522667

ISBN: (纸本)9798331522674

Efficient classification of research papers is crucial for managing the growing volume of academic literature. Traditional single-label methods fall short in capturing interdisciplinary content, emphasizing the need for multilabel classification. this project's findings address existing limitations and provide a pathway for better knowledge discovery and research efficiency. To overcome these limitations, this study utilized multilabel classification through advanced transformer models such as BERT, RoBERTa, ALBERT, XLNet, DistilBERT, and XLM. Among these, RoBERTa achieved the highest accuracy of 92.76%, followed by ALBERT (92.19%), XLNet (92.17%), BERT (92.06%), and DistilBERT (92.18%). In contrast, XLM lagged significantly with 79.09%. Hybrid transformer models performed worse than standalone ones. this automated classification system enhances the efficiency of literature reviews, making it easier to access relevant research material quickly. Furthermore, by offering a more precise categorization of research articles, it supports academic institutions and researchers in identifying the most pertinent studies across disciplines. this advancement not only improves research productivity but also encourages collaborative efforts by reducing the time spent on manual classification. the ability to accurately classify research papers facilitates more efficient access to relevant information, supporting researchers in their efforts to stay abreast of advancements in their fields. Additionally, the approach has wide applicability, particularly in domains where timely and accurate information is critical, such as healthcare and policy-making.

关键词： Technological innovation Accuracy Collaboration Bidirectional control Multi label classification Transformers Natural language processing Encoding Reliability Systematic literature review

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An arXiv Paper Question-Answering System Based on Qwen and RAG

An arXiv Paper Question-Answering System Based on Qwen and R...

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Frontiers technology of information and Computer (ICFTIC), IEEE international conference on

作者： Jiakai Gu Donghong Qin School of Artificial Intelligence Guangxi Minzu University Nanning Guangxi China

ISBN: (数字)9798331541750

ISBN: (纸本)9798331541767

this paper explores combining large language models and retrieval-augmented generation (RAG) techniques to build a question-answering system for academic papers. the system is built upon Qwen2.5 models and open-source tools like LlamaIndex, utilizing the arXiv dataset to implement an end-to-end pipeline from user queries to relevant answers through models including query routing, hybrid retrieval, and answer generation. In the retrieval phase, the system leverages both the BGE-M3 embedding model and the BGE-Reranker-V2-M3 reranking model. the generation stage integrates knowledge from multiple sources using a fine-tuned Qwen2.5 model. Experiments show significant improvements in retrieval accuracy and answer coherence by incorporating Reranker and RAG-Fusion. this study highlights the potential of LLM and RAG technologies in academic applications and offers insights for enhancing paper question-answering systems.

关键词： Visualization Technological innovation Query processing Large language models Scalability Retrieval augmented generation Pipelines Hybrid power systems Multilingual Prompt engineering

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Enhancing language Representation with Emotional Intelligence: A Weighted TF-IDF Approach

Enhancing Language Representation with Emotional Intelligenc...

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international conference on Contemporary Computing and Informatics (IC3I)

作者： Gurleen Kaur Gurleen Singh Pulkit Dwivedi Apex Institute of Technology(CSE) Chandigarh University Punjab India

In the era of vast data availability, harnessing the power of smart automation and computational analysis is crucial for understanding public opinions in real time. Emotional intelligence technology plays a significant role in this intelligence landscape, and its research is pivotal in gaining insightful perspectives. Traditional scientific research often relies on representation classification, which primarily focuses on the semantic knowledge of words while neglecting their emotional dimensions. this study highlights the potential of sentiment analysis as a promising area of investigation and emphasizes the need to develop robust and precise methods that can effectively handle the complexities of natural language and capture the nuances of human emotions and sentiments. To address this, we propose a language representation enhancement technique that integrates emotional knowledge into the TF-IDF algorithm, resulting in weighted language representations. Furthermore, we explore the performance of Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) in the field of Natural language processing (NLP). We find that while both architectures have demonstrated exceptional performance, RNNs, with their ability to predict longer-term dependencies, offer a more comprehensive understanding of language and provide richer information compared to CNNs. Future research should focus on refining these techniques and advancing our understanding of emotional intelligence in computational analysis.

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Locate and Label: A Two-stage Identifier for Nested Named Entity Recognition 59

Locate and Label: A Two-stage Identifier for Nested Named En...

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作者： Shen, Yongliang Ma, Xinyin Tan, Zeqi Zhang, Shuai Wang, Wen Lu, Weiming Zhejiang Univ Coll Comp Sci & Technol Hangzhou Peoples R China Univ Sci & Technol China Hefei Peoples R China

ISBN: (纸本)9781954085527

Named entity recognition (NER) is a well-studied task in natural language processing. Traditional NER research only deals with flat entities and ignores nested entities. the span-based methods treat entity recognition as a span classification task. Although these methods have the innate ability to handle nested NER, they suffer from high computational cost, ignorance of boundary information, under-utilization of the spans that partially match with entities, and difficulties in long entity recognition. To tackle these issues, we propose a two-stage entity identifier. First we generate span proposals by filtering and boundary regression on the seed spans to locate the entities, and then label the boundary-adjusted span proposals with the corresponding categories. Our method effectively utilizes the boundary information of entities and partially matched spans during training. through boundary regression, entities of any length can be covered theoretically, which improves the ability to recognize long entities. In addition, many low-quality seed spans are filtered out in the first stage, which reduces the time complexity of inference. Experiments on nested NER datasets demonstrate that our proposed method outperforms previous state-of-the-art models.

关键词： Natural language processing systems

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AI-Powered Legal Documentation Assistant

AI-Powered Legal Documentation Assistant

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Pervasive Computing and Social Networking (ICPCSN), international conference on

作者： Kuldeep Vayadande Aditi Bhat Pranav Bachhav Aditya Bhoyar Zulfikar Charoliya Aayush Chavan Department of Information Technology Vishwakarma Institute of Technology Pune India

ISBN: (数字)9798350386349

ISBN: (纸本)9798350386356

Legal systems worldwide vary in structure and principles, reflecting the diverse legal traditions of different countries. the legal system, inherently complex and reliant on meticulous documentation, often faces challenges related to time-consuming manual processes and the potential for human errors. the system proposed provides a transformative solution to the above problems. the system emerges as a groundbreaking solution within the intricate landscape of legal systems which responds to these challenges by seamlessly integrating advanced AI techniques. At its core, OpenAI embeddings takes center stage, demonstrating unparalleled proficiency in document generation, comprehension, and abnormality detection, addressing the complexities ingrained in legal documentation. In contrast to traditional approaches, this system maximizes the versatility of ChatGPT 3.5, allowing it to not only issue commands but also proficiently generate a diverse array of legal documents. By incorporating an understanding module equipped with PyPDF, Amazon Textract, and langchain utilities, the system adeptly handles document intricacies. the utilization of OpenAI Embeddings further enhances natural language understanding. Leveraging sentiment analysis and Named Entity Recognition (NER) in its natural language processing (NLP) toolkit, the system employs an intuitive web interface for irregularities detection. the exploration of AI for automated irregularity detection showcases its transformative potential in ensuring document accuracy within the legal domain. this project, therefore, stands as a beacon of innovation, promising to reshape the dynamics of legal document processing by merging advanced AI capabilities with the unique demands of legal systems.

关键词： Pervasive computing Technological innovation Sentiment analysis Law Social networking (online) Documentation Named entity recognition

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Use of Neural Machine Translation in Multimodal Translation

Use of Neural Machine Translation in Multimodal Translation

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international conference on Contemporary Computing and Informatics (IC3I)

作者： Manavi Nair Sarvesh Tanwar Sumit Badotra Vinay Kukreja Amity Institute of Information Technology Amity University Noida Uttar Pradesh India School of Computer Science and Engineering Bennett University Greater Nouda U.P India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

Multimodal Neural Machine Translation (MNMT) is a type of Machine Translation that allows the translation of source language that contains various forms of information, such as images, video, audio, and signs, into the target language while maintaining the intended meaning of the source language. In MNMT, the use of deep learning techniques such as Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) are required to process different modalities and to generate an appropriate translation. the use of attention mechanisms, early and late fusion techniques, and hierarchical fusion have significantly improved the accuracy and fluency of translation. the performance of different MNMT systems can be evaluated in terms of various metrics such as BLEU score, accuracy, and fluency. MNMT has the potential to revolutionise the way we communicate by bridging the gap between different languages and cultures. the integration of MNMT in various industries, such as e-commerce, tourism, and healthcare, can bring significant benefits in terms of customer experience and accessibility. It states how Neural Machine Translation plays a significant role in processing different modalities in Multimodal Neural Machine Translation. More research work in this field can produce effective and innovative ideas to improve Multimodal Neural Machine Translation.

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