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Exploring the natural language Generation: Current Trends and Research Challenges

Exploring the Natural Language Generation: Current Trends an...

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2024 international conference on engineering and Computing, ICECT 2024

作者： Azhar, Usama Nazir, Ambreen Shifa Tameer-e-Millat University Department of Computing Islamabad Pakistan

ISBN: (纸本)9798350349719

natural language Generation (NLG) acts as a bridge between input data and human communication. NLG systems are meant to generate human-understandable output making it a useful technique mainly in report generation, automatic replies to queries like Chatbots, and other domains that involve natural language processing (NLP). NLG continues to evolve;embracing the latest trends like neural networks, large language models, personalized features, cognitive architectures, and emotional intelligence, but it still faces some challenges with its implementations and operations for cohesive integration. Therefore, this survey presents a brief introduction to some of the latest trends and challenges faced by NLG which are playing a dynamic role in shaping the evolution of NLG. © 2024 ieee.

关键词： Recurrent neural networks

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The Design and Implementation of APLOS: An Automated PoLicy DOcument Summarisation System 2

The Design and Implementation of APLOS: An Automated PoLicy ...

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2nd international conference on Foundation and Large language Models, FLLM 2024

作者： Sowe, Sulayman Kiel, Tobias Neumann, Alexander Mou, Yongli Peristeras, Vassilios Decker, Stefan RWTH Aachen University Informatik 5 Department of Computer Science Aachen52074 Germany Fraunhofer FIT Sankt Augustin53757 Germany International Hellenic University School for Science and Technology Thessaloniki57001 Greece

ISBN: (纸本)9798350354799

Many institutions write and publish policy documents to inform stakeholders or citizens about priority areas and legislations or regulations governing issues such as the environment, agriculture, food safety and business operations, to mention a few. These documents are often lengthy, have different formats, are full of jargon, may have many versions, and require domain knowledge to understand the policy issue. These characteristics make it challenging to develop a personalised tool for users to generate concise summaries based on their domain knowledge, refine the summaries without compromising the original meaning of the policy, and find related policies. To solve these problems, we leverage advances in foundation models (FMs) and Large language Models (LLMs) to develop a tool called "APLOS"for summarising and gaining insights from policy documents. The architecture, implementation, and natural language processing challenges we faced in developing the tool and the future directions we are undertaking to address the challenges are presented and discussed. © 2024 ieee.

关键词： natural language processing systems

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Improving Open-Ended Referring Expression Comprehension via Dual-language Constraints

Improving Open-Ended Referring Expression Comprehension via ...

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

作者： Lei, Chenglong Yang, Chao Zheng, Jianbo Hunan University Changsha China

ISBN: (纸本)9798350368741

Open-ended referring expression comprehension focuses on locating the text query within an image via scene knowledge, requiring complex reasoning across the triplet of the image, scene knowledge, and the text query. However, most existing methods struggle to integrate scene knowledge while performing a single-object prediction. To address this issue, we propose VG-DLC, a model that progressively uses scene knowledge and open-ended referring expressions as constraints for reasoning and grounding. Specifically, we first align scene knowledge with the referring expression and the image in sequence, which supports effective relational reasoning and implicitly constrains the open-ended content to corresponding image regions. Next, we explicitly align the referring expression with the image and employ it as a query vector, constraining it to a unique region to achieve a single end-to-end prediction. Extensive experiments on the SK-VG dataset validate the effectiveness of our method, demonstrating that VG-DLC outperforms existing end-to-end approaches. © 2025 ieee.

关键词： language Constraint Open-ended Referring Expression Comprehension Visual Grounding

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Enhancing natural language processing with Deep Learning for Sentiment Analysis 9

Enhancing Natural Language Processing with Deep Learning for...

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9th international conference on Science, Technology, engineering and Mathematics, ICONSTEM 2024

作者： Om Prakash, C. Kumar, S. Vijaya Pallavi Abbas, Jamal K. Naser, Sarmad Jaafar Haroon, Noor Hanoon School of Management Cmr University 5 Bhuvanagiri OMBR Layout Karnataka Bengaluru560043 India B S Abdur Rahman Crescent Institute of Science And Technology Chennai Tamil Nadu Vandalur India Chaudhary Ranbir Singh University Department of English Haryana Jind India AL-Nisour University College Baghdad Iraq College of Pharmacy National University of Science And Technology DhiQar Iraq Technical Engineering College Al-Ayen University Department of Computer Technical Engineering Thi-Qar Iraq

ISBN: (纸本)9798350365092

natural language processing (NLP) aids in the advancement of intelligent machines through its emphasis on etymologically grounded human-PC connections and a greater understanding of the human language. The demand and necessity to implement information-driven methodologies for automating semantic analysis have increased due to ongoing advancements in processing power and the accessibility of vast quantities of phonetic data. The application of deep learning methods has facilitated significant progress in fields including computer vision, programmed discourse recognition, and natural language processing, which has led to the pervasive adoption of information-driven techniques. This study investigates calculations for unimodal and multimodal sentiment analysis in interpersonal organisations and develops two models, one for message sentiment analysis and the other for picture-message multimodal sentiment analysis in informal communities. The two models demonstrated their electiveness by surpassing the benchmark models by 4.45% and 5.2%, respectively, when evaluated against the current models on distinct datasets. © 2024 ieee.

关键词： Sentiment analysis

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Analysis of natural language processing Techniques and Tools for Requirements Elicitation: A Systematic Literature Review 12

Analysis of Natural Language Processing Techniques and Tools...

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12th international conference in Software engineering Research and Innovation, CONISOFT 2024

作者： Torres-Igartua, Maria Jose Sanchez-Garcia, Angel J. Ocharan-Hemandezss, Jorge Octavio Vazquez-Reyest, Sodel Universidad Veracruzana Facultad de Estadística e Informática Veracruz Xalapa Mexico Unidad Académica de Ingeniería Eléctrica Universidad Autónoma de Zacatecas Zacatecas Mexico

ISBN: (纸本)9798331532116

Requirements engineering (RE) encompasses ac-tivities such as requirements elicitation, analysis, specification, and validation, which are essential in software development for defining and aligning stakeholder needs and expectations. However, these processes are often laborious and prone to errors and misunderstandings, resulting in systems that fail to meet expectations and require costly revisions. This study specifically focuses on requirements elicitation, as accurate and comprehensive gathering of requirements is vital for the success of subsequent activities. The aim is to explore how natural language processing (NLP) techniques and tools can enhance requirements elicitation, emphasizing their adapt-ability to diverse linguistic contexts. A Systematic Literature Review (SLR) identified the most commonly used Artificial Intelligence (AI) techniques in NLP, such as Support Vector Machine (SVM), Random Forest (RF), and Transformer-based approaches like Bidirectional Encoder Representations from Transformers (BERT). The natural language Toolkit (NLTK) is recognized as a prominent tool in the broader NLP domain, while the ELICitation Aid (ELICA) tool stands out for its application in requirement elicitation. Despite the advantages offered by these approaches, it is crucial to acknowledge and address their limitations. Further research is recommended to enhance their effectiveness, particularly in adapting to languages other than English. © 2024 ieee.

关键词： Software design

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Towards Understanding and Analyzing Rationale in Commit Messages using a knowledge Graph Approach

Towards Understanding and Analyzing Rationale in Commit Mess...

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ACM/ieee international conference on Model Driven engineering languages and Systems (MODELS)

作者： Dhaouadi, Mouna Oakes, Bentley James Famelis, Michalis Univ Montreal DIRO Montreal PQ Canada Polytech Montreal GIGL Montreal PQ Canada

ISBN: (纸本)9798350324983

Extracting rationale information from commit messages allows developers to better understand a system and its past development. Here we present our ongoing work on the Kantara end-to-end rationale reconstruction pipeline to a) structure rationale information in an ontologically-based knowledge graph, b) extract and classify this information from commits, and c) produce analysis reports and visualizations for developers. We also present our work on creating a labelled dataset for our running example of the Out-of-Memory component of the Linux kernel. This dataset is used as ground truth for our evaluation of NLP classification techniques which show promising results, especially the multi-classification technique XGBoost.

关键词： rationale structuring rationale extraction natural language processing Linux ontology dataset openCAESAR

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Ansible Lightspeed: A Code Generation Service for IT Automation 24

Ansible Lightspeed: A Code Generation Service for IT Automat...

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39th ACM/ieee international conference on Automated Software engineering (ASE)

作者： Sahoo, Priyam Pujar, Saurabh Nalawade, Ganesh Gebhardt, Richard Mandel, Louis Buratti, Luca Red Hat Raleigh NC USA IBM Res Yorktown Hts NY 10598 USA

ISBN: (数字)9798400712487

ISBN: (纸本)9798400712487

The availability of Large language Models (LLMs) which can generate code, has made it possible to create tools that improve developer productivity. Integrated development environments or IDEs which developers use to write software are often used as an interface to interact with LLMs. Although many such tools have been released, almost all of them focus on general-purpose programming languages. Domain-specific languages, such as those crucial for Information Technology (IT) automation, have not received much attention. Ansible is one such YAML-based IT automation-specific language. Ansible Lightspeed is an LLM-based service designed explicitly to generate Ansible YAML, given natural language prompt. In this paper, we present the design and implementation of the Ansible Lightspeed service. We then evaluate its utility to developers using diverse indicators, including extended utilization, analysis of user edited suggestions, as well as user sentiments analysis. The evaluation is based on data collected for 10,696 real users including 3,910 returning users. The code for Ansible Lightspeed service and the analysis framework is made available for others to use. To our knowledge, our study is the first to involve thousands of users of code assistants for domain-specific languages. We are also the first code completion tool to present N-Day user retention figures, which is 13.66% on Day 30. We propose an improved version of user acceptance rate, called Strong Acceptance rate, where a suggestion is considered accepted only if less than 50% of it is edited and these edits do not change critical parts of the suggestion. By focusing on Ansible, Lightspeed is able to achieve a strong acceptance rate of 49.08% for multi-line Ansible task suggestions. With our findings we provide insights into the effectiveness of small, dedicated models in a domain-specific context. We hope this work serves as a reference for software engineering and machine learning researchers exploring code c

关键词： Productivity Sentiment analysis Codes Automation Large language models natural languages Machine learning Software Software engineering Domain specific languages

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Cross-Lingual Sentiment Prediction with Various Classifiers for Structural Correspondence Learning and Synonym Augmentation 1

Cross-Lingual Sentiment Prediction with Various Classifiers ...

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1st international conference for Women in Computing, InCoWoCo 2024

作者： Meghana, D.K. Kiran, K. Harshala, R. Shenoy, P. Deepa Venugopal, K.R. University Visvesvaraya College of Engineering Bangalore University Department of Computer Science & Engineering Bengaluru India

ISBN: (纸本)9798331518943

The Cross-Lingual Sentiment Analysis(CLSA) is a process of utilizing information from resource rich language to predict sentiment of resource poor languages. The monolingual approaches based on machine learning cannot be viably applied for resource-scarce domain as sufficient information is not available. This has picked up specific interest in the field on natural language processing. Subsequently, this article proposes Structural Correspondence Learning (SCL) that utilizes basic correspondence for cross-lingual opinion investigation with synonyms. The key thought behind SCL, is to exploit the knowledge from the source language space to target language space by minimizing loss of data[1]. In contrast to previous SCL methods where it uses logistic classifier to predict, here different classifiers like Random Forest, Gaussian Naïve Bayes, Bernoulli NB, Multinomial NB, K- nearest Neighbors and Decision Tree are trained. This work comprises of two categories: one, training the model with synonyms of pivot for different classifiers and another one, training the model without synonyms on target language using different classifiers. The experimental results show that target language appended with synonyms performs better compared to the one without synonyms for Random Forest. © 2024 ieee.

关键词： Contrastive Learning

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Research on Deaf-Mute People Translation System Based on natural language processing 5

Research on Deaf-Mute People Translation System Based on Nat...

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5th international conference on Artificial Intelligence and Computer engineering, ICAICE 2024

作者： Zhang, Tianyi Liao, Sherry Shanghai No.8 High School Shanghai China Qsi International School Shenzhen China

ISBN: (纸本)9798331528911

Sign language is a vital mode of communication for deaf individuals, yet it frequently proves challenging to convey messages to hearing people. The preceding two-way interaction system was found to be ineffective. Furthermore, convolutional neural network-based devices are also susceptible to interference in complex environments, which affects the recognition effect. The objective of the model proposed in this work is to address the communication barriers between deaf-mute and hearing-friendly people, primarily through the utilisation of sign language interpretation. The initial stage of the process involved the utilisation of a convolutional neural network (CNN) to facilitate gesture recognition, capture the intricate details of sign language actions, and integrate deep learning methodologies to extract the distinctive features of gesture images. Subsequently, a natural language processing model is employed to combine the visual features with the language model, thereby facilitating accurate translation from sign language to spoken language. To enhance recognition capabilities in complex settings, the system employs multimodal data fusion technology, integrating gesture actions and contextual semantic data to bolster the reliability and precision of translation. The experimental analysis demonstrates that the model exhibits high accuracy and efficiency in translation and interaction across a range of scenarios. © 2024 ieee.

关键词： Convolutional neural networks

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M-segclip: Enhancing SegCLIP with RM-MLP for Open-Vocabulary Semantic Segmentation 2

M-segclip: Enhancing SegCLIP with RM-MLP for Open-Vocabulary...

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2nd international conference on Algorithm, Image processing and Machine Vision, AIPMV 2024

作者： Zhang, Yuhang Che, Yanqiu Li, Shanshan Wang, Ruofan School of Automation and Electrical Engineering Tianjin University of Technology and Education Tianjin Key Laboratory of Information Sensing & Intelligent Control Tianjin China School of Information Technology Engineering Tianjin University of Technology and Education Tianjin China

ISBN: (纸本)9798350390254

With the emergence of large-scale visual language models represented by CLIP, we have broken the great divide between vision and language. It has become possible to project features from visual models to semantic space consisting of a large amount of text. At the same time, semantic segmentation has opened the door to transferring learned visual knowledge to open vocabulary semantic segmentation. In this paper we improve and optimize a model with open vocabulary segmentation capability based on the CLIP architecture, we design an RM-MLP module and insert this module after the semantic group module in the image encoder, which is used to optimize the semantic regions aggregated by the semantic group module, and then used to generate the final segmentation results. The final experimental results show that the optimized model achieves higher segmentation accuracy on PASCAL VOC 2012 (+1.14% mIoU), and COCO (+0.43% mIoU). © 2024 ieee.

关键词： Semantic Segmentation

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