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Automation for Requirements engineering: Opportunities and Pitfalls - RE '24 Keynote 32

Automation for Requirements Engineering: Opportunities and P...

引用

32nd ieee international Requirements engineering conference (RE)

作者： Mendling, Jan Humboldt Univ Dept Comp Sci Berlin Germany

ISBN: (纸本)9798350395129;9798350395112

Recent technologies like ChatGPT inspire ideas of increasing automation for professional tasks in various domains. This includes among others requirements engineering. So far, however, the divide between useful and harmful automation has been blurry and in flux. In this keynote, I will discuss foundational concepts with which automation has been analyzed. Using these concepts, I will discuss the opportunities arising for requirements engineering from the advent of natural language processing, image processing, and event sequence analysis techniques. Furthermore, I will discuss important pitfalls that have been well documented for other automation technologies in the past.

关键词： Requirements engineering

来源：评论

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Communication Assistance for Blind and Deaf using natural language processing

Communication Assistance for Blind and Deaf using Natural La...

引用

2024 international conference on knowledge engineering and Communication Systems, ICKECS 2024

作者： Sangeetha, V. Pani, Ishwariya Yasaswini, K. Nikita, B. Bhattacharjee, Arpita Ramaiah Institute of Technology Department of Cse Bangalore India

ISBN: (纸本)9798350359688

Having difficulties like being visually impaired, hard of listening to, dumb are a more amount of difficulty. Technology and innovation have motivated humans to grow to be depending on solace but there exists an underprivileged growth of those who are fighting for finding a creative manner that could make the procedure of conversation less difficult for them. The traditional ways of communication and knowing any information for disabled people is very time consuming as they had to find someone to ask for the information and wait for their response, and accurate answers are not received and language or gestures act as barriers. To overcome these problems, in this paper natural language processing(NLP) and Speech Recognition is used. NLP and Speech recognition eradicate language barriers between the disabled and the non-disabled person. The main objectives is to convert the spoken audio to text format, speech-to-text is used and to convert the typed text to audio format, text-to-speech is used. Speech to text and text to speech features in the website application are faster. This application gives a user friendly platform for the blind and deaf. © 2024 ieee.

关键词： Speech recognition

来源：评论

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language-DRIVEN OPEN-VOCABULARY 3D SEMANTIC SEGMENTATION WITH knowledge DISTILLATION 49

LANGUAGE-DRIVEN OPEN-VOCABULARY 3D SEMANTIC SEGMENTATION WIT...

引用

49th ieee international conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Wu, Yuting Han, Xian-Feng Xiao, Guoqiang SouthWest Univ Coll Comp & Informat Sci Chongqing Peoples R China

ISBN: (纸本)9798350344868;9798350344851

3D open-vocabulary semantic segmentation is a challenge in the task of 3D scene understanding, as most current models trained on closed-set datasets struggle to effectively identify categories that were not seen during training. To address this, we introduce a framework called LSWKD. It distills knowledge from a pre-trained 3D open-world model, thereby enhancing the alignment between visual and semantic features. Furthermore, we employ Point-discriminative Contrastive Learning to compute caption loss in the teacher model instead of CLIP-style Contrastive Loss in order to let each point be supervised with its all related language captions, which improves the teacher model's performance. We conducted experiments on ScanNet and S3DIS datasets. The results demonstrate that our approach achieves better hIoU compared with state-of-the-art models. Code will be released at https://***/wu39848/LSWKD.

关键词： 3D open-vocabulary semantic segmentation Vision-language model knowledge Distillation Contrastive Learning

来源：评论

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RETRIEVAL-GENERATION SYNERGY AUGMENTED LARGE language MODELS 49

RETRIEVAL-GENERATION SYNERGY AUGMENTED LARGE LANGUAGE MODELS

引用

49th ieee international conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Feng, Zhangyin Feng, Xiaocheng Zhao, Dezhi Yang, Maojin Qin, Bing Harbin Inst Technol Harbin Peoples R China Pengcheng Lab Shenzhen Peoples R China

ISBN: (纸本)9798350344868;9798350344851

Large language models augmented with task-relevant documents have demonstrated impressive performance on knowledge-intensive tasks. However, regarding how to obtain effective documents, the existing methods are mainly divided into two categories. One is to retrieve from an external knowledge base, and the other is to utilize large language models to generate documents. We propose an iterative retrieval-generation collaborative framework. It is not only able to leverage both parametric and non-parametric knowledge, but also helps to find the correct reasoning path through retrieval-generation interactions, which is very important for tasks that require multi-step reasoning. We conduct experiments on four question answering datasets, including single-hop QA and multi-hop QA tasks. Empirical results show that our method significantly improves the reasoning ability of large language models and outperforms previous baselines.

关键词： large language models retrieval augmented question answering

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MLLMIE: Multiple Large language Model Information Extraction in Ancient Medical Texts 7

MLLMIE: Multiple Large Language Model Information Extraction...

引用

7th international conference on Machine Learning and natural language processing, MLNLP 2024

作者： Wang, Feilong Shi, Donghui Cui, Xinyi School of Electronics and Information Engineering Anhui Jianzhu University Mass Spectrometry Key Technology R&D and Clinical Application of Anhui Province Jointly Constructed Discipline Key Experiments Hefei China School of Electronics and Information Engineering Anhui Jianzhu University Hefei China

ISBN: (纸本)9798350354973

Ancient medical texts, which reflect traditional cultural practices, can play an important role in the promotion of medical traditions. Nevertheless, the complexity of extracting information from classical Chinese medical texts and the potential inconsistencies and hallucinations in large language models during information extraction present significant challenges. In domains such as medicine, where the precision of information extraction is of paramount importance, the paper augmented existing methodologies for information extraction from large language models and proposed a novel approach, designated as Multiple Large language Model Information Extraction (MLLMIE). The method initially defined a pattern layer to explicitly delineate the concepts, terms, and their interrelationships within the text. Subsequently, it employed prompt engineering to extract information from multiple models, thereby obtaining candidate medical ancient text knowledge results. Finally, the model validation layer was employed to filter the candidate results, thereby obtaining the final medical ancient text knowledge. The method attains F1 scores of 92.68% for named entity recognition and 94.91% for relation extraction in ancient medical texts, thereby demonstrating high-quality medical ancient text information extraction. This method effectively enhances the accuracy of ancient medical text information extraction and facilitates the discovery and application of ancient medical text knowledge. © 2024 ieee.

关键词： Information retrieval

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Automatic Domain-specific Corpora Generation from Wikipedia - A Replication Study 31

Automatic Domain-specific Corpora Generation from Wikipedia ...

引用

31st ieee international Requirements engineering conference (RE)

作者： Ruwanpura, Seniru Morash, Cale Khan, Momin Ali Ahmad, Adnan Ginde, Gouri Univ Calgary Dept Elect & Software Engn Calgary AB Canada

ISBN: (纸本)9798350326918

Replication studies help mature our knowledge and attempt to validate the findings of a prior piece of research. However, these studies are still rare in the Requirements engineering field. Additionally, the rapidly advancing realm of natural language processing (NLP) is creating new opportunities for efficient, machine-assisted workflows application which can bring new perspectives and results to the forefront. Thus, in this paper, we replicate and extend a previous study (baseline), a tool, VVikiDoMiner, which automatically generated domain-specific corpora by crawling Wikipedia. In this study, we investigated and executed the implementation of WikiDoMiner (open-sourced code from the original paper) to recreate the results. This allowed us to strengthen the external validity of the original study. We extended the baseline to evaluate additional data sets and generated nuanced results using state-of-the-art NLP techniques such as Bidirectional Encoder Representations from Transformers (BERT). Results showed that due to the growing content in Wikipedia, the corpus generated for the Railways and Networks domains did not precisely match the results from the baseline. However, utilizing the state-of-the-art KeyBERT library from the Huggingface AI community enhanced the results, eventually generating a meaningful corpus compared to the baseline.

关键词： Replication study Requirements analysis Requirements engineering natural language processing BERT

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INCOMPLETE OBSERVATIONS BIAS SUPPRESSION FOR ABDUCTIVE natural language INFERENCE 49

INCOMPLETE OBSERVATIONS BIAS SUPPRESSION FOR ABDUCTIVE NATUR...

引用

49th ieee international conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Gu, Yu Luo, Xianlong Yang, Meng Sun Yat Sen Univ Sch Comp Sci & Engn Guangzhou Peoples R China

ISBN: (纸本)9798350344868;9798350344851

Abductive natural language commonsense reasoning is a task aiming at inferring the most plausible explanation in narrative text for observed events. Previous works mostly concentrate on utilizing powerful pre-trained language models and making better use of excess training data to learn abundant event commonsense knowledge. However, the utilization of causal effect is hidden in the language reasoning process and the explicit constraint of the causal effect between events has not been explored, resulting in biased inference. The model may focus on one observed event and make the wrong prediction while ignoring the other helpful events. To reveal the problem we modify the original task by appending unrelated text to the context which won't change the causal relation. And typical methods get worse in the new task as they are not good at utilizing the complementary between the two observations. Motivated by eliminating the shortcut from incomplete observation and utilizing the complementarity of the two observations, we propose an incomplete observation bias suppression method to guide the training process. Results show our approach can ease the problem revealed in the new task. Based on the proposed method and the new task, our method also get competitive result on the original task.

关键词： natural language Inference Bias Suppression Context splicing Pre-trained methods

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Research on Computer Intelligent ChatGPT natural language processing System Based on Scientific knowledge Graph 24

Research on Computer Intelligent ChatGPT Natural Language Pr...

引用

international conference on Machine Intelligence and Digital Applications (MIDA)

作者： Zhan, Liuchun Huang, Changjiang Guangzhou Coll Appl Sci & Technol Coll Comp Sci Guangzhou Peoples R China Guangzhou Coll Appl Sci & Technol Res Base Guangdong Social Sci Federat Urban & Rur Guangzhou Peoples R China

ISBN: (纸本)9798400718144

A synonym mining method is proposed by combining the character vector graph and noise robust learning method. The model uses paired word vectors pre-trained by ChatGPT to enhance entity semantic representation. Classify marks with noise. Then the cross optimal processing is carried out to identify the true and false marks. The two-layer construction system of knowledge extraction and knowledge fusion is constructed to realize the independent construction and answer of software engineering questions. The system effectively improves the efficiency of software project understanding and software reuse.

关键词： Software knowledge graph ChatGPT Software knowledge extraction natural language processing system

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Translating natural language Queries to SQL Using the T5 Model 18

Translating Natural Language Queries to SQL Using the T5 Mod...

引用

18th Annual ieee international Systems conference (SysCon)

作者： Wong, Albert Pham, Lien Lee, Young Chan, Shek Sadaya, Razel Khmelevsky, Youry Clement, Mathias Cheng, Florence Wing Yau Mahony, Joe Ferri, Michael Langara Coll Math & Stat Vancouver BC Canada Okanagan Coll Math & Stat Kelowna BC Canada Okanagan Coll Comp Sci Kelowna BC Canada Harris SmartWorks Res & Dev Ottawa ON Canada

ISBN: (纸本)9798350358810;9798350358803

This paper presents the development process of a natural language to SQL model using the T5 model as the basis. The models, developed in August 2022 for an online transaction processing system and a data warehouse, have a 73% and 84% exact match accuracy respectively. These models, in conjunction with other work completed in the research project, were implemented for several companies and used successfully on a daily basis. The approach used in the model development could be implemented in a similar fashion for other database environments and with a more powerful pre-trained language model.

关键词： natural language processing Data Query System Text-to-SQL Speech-to-SQL Deep Learning Machine Learning T5 Model Human-Machine-Systems Energy Systems

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Analyzing Unstructured Data: natural language processing Frameworks, Challenges, Approaches, and Applications 4

Analyzing Unstructured Data: Natural Language Processing Fra...

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4th ieee international conference on ICT in Business Industry and Government, ICTBIG 2024

作者： Akhil, Karnam Vamsi, Tamma Bhuwan Soujanya, Somaram Harshitha, Kambhampati Kodanda Sai Mounisha, Miryala Srihitha, Nimma Vnr Vignana Jyothi Institute of Engineering and Technology Hyderabad India ServiceNow Hyderabad India

ISBN: (纸本)9798331518981

Today's world data is about 80-90% unstructured, which makes it very complex to analyze, interpret and understand. Meaningful insights, patterns are hidden in the data, discovering knowledge and converting to structured form is important. It is also important to interpret in various forms and process the data. In this paper, we have presented an overview which includes various texts, their representation formats and finds natural language processing (NLP), an essential component for interpreting the unstructured form. NLP components including its history, components, architecture, and challenges are well illustrated. Presented Research dives the evolution of NLP from rule-based systems to modern Deep Learning (DL) approaches. Key features and characteristics such as natural language Understanding (NLU), natural language Generation (NLG), and machine translation are discussed. The architectural framework of NLP systems is presented, furthermore, we address research and industrial challenges in NLP, emphasizing the need for robust methodologies, domain-specific changes, and ethical considerations. © 2024 ieee.

关键词： Deep learning

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