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Classifying Ambiguous Requirements: An Explainable Approach in Railway Industry 32

Classifying Ambiguous Requirements: An Explainable Approach ...

引用

32nd ieee international Requirements engineering conference (RE)

作者： Beqiri, Lodiana Montero, Calkin Suero Cicchetti, Antonio Kruglyak, Andrey Malardalen Univ Vasteras Sweden Uppsala Univ Uppsala Sweden Alstom Vasteras Sweden

ISBN: (纸本)9798350395525;9798350395518

A clear understanding of customers' requirements is fundamental towards developing products that behave as intended. Customers commonly use natural language (NL) to specify their requirements. As NL is innately ambiguous and an industrial project could contain thousands of specifications, requirement analysis becomes a highly demanding and time-consuming task. One of the goals in industry is, therefore, to minimise the amount of time spent manually analysing requirements. This article presents a natural language processing (NLP) approach to automatically classify rail domain requirements based on the presence of ambiguity, and to provide textual explanations regarding the reason behind the classification. Traditional machine learning (ML) classification models are trained using lexical features from requirements and experts' comments concerning ambiguity on annotated real-world data. 10-fold cross-validation results show an F-score up to 0.87, with a recall up to 0.88. Furthermore, a validation of the model with 100 additional requirements achieved an accuracy of 0.78, with 76% match between the model's and expert's classification. The provided explanations are important for the expert in understanding the key decision terms involved in the classification, as well as provided insights on the presence of ambiguities in requirements. Ours is among the first works that uses explainability to tackle ambiguity in textual requirements, employing NLP and ML.

关键词： requirements analysis ambiguity natural language processing explainability

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Supervised Contrastive Learning for Short Text Classification in natural language processing 14

Supervised Contrastive Learning for Short Text Classificatio...

引用

14th international conference on Computer and knowledge engineering, ICCKE 2024

作者： Esmaeili, Mitra Vahdat-Nejad, Hamed Faculty of Electrical and Computer Engineering University of Birjand Birjand Iran

ISBN: (纸本)9798331511272

In recent years, the swift progress in information retrieval technologies has positioned text classification as a key area of research. Classifying short texts represents a major challenge within natural language processing. Given the growing prevalence of social media during critical events like hurricanes, accurately categorizing these texts is essential for facilitating relief operations. Tweets are concise and have an informal tone, which creates unique challenges for effective classification. Supervised contrastive learning has recently become popular as a strong machine learning method. It offers significant improvements over traditional approaches, especially in the area of natural language processing. This paper introduces a supervised contrastive learning methodology designed to enhance the accuracy of short-text classification while maintaining the model’s generalization capability. Our approach consistently surpasses existing state-of-the-art techniques, delivering better accuracy and stability across different ranges of text classification tasks. ©2024 ieee.

关键词： Contrastive Learning

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Use of natural language processing in Digital engineering Context to Aid Tagging of Model 17

Use of Natural Language Processing in Digital Engineering Co...

引用

17th Annual ieee international Systems conference (SysCon)

作者： Dunbar, Daniel Vierlboeck, Maximilian Blackburn, Mark Stevens Inst Technol Sch Syst & Enterprises Hoboken NJ 07030 USA

ISBN: (纸本)9781665439947

This paper uses natural language processing to provide augmented intelligence assistance to the resource intensive task of aligning systems engineering artifacts, namely text requirements and system models, with ontologies. Ontologies are a key enabling technology for digital, multidisciplinary interoperability. The approach presented in this paper combines the efficiency of statistical based natural language processing to process large sets of data with expert verification of output to enable accurate alignment to ontologies in a time efficient manner. It applies this approach to an example from the telecommunications domain to demonstrate the workflows and highlight key points in the process. Enabling easier, faster alignment of systems engineering artifacts with ontologies allows for a holistic view of a system under design and enables interoperability between tools and domains.

关键词： ontology natural language processing semantic web digital engineering authoritative source of truth augmented intelligence

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AI-based Question Answering Assistance for Analyzing natural-language Requirements 23

AI-based Question Answering Assistance for Analyzing Natural...

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45th ieee/ACM international conference on Software engineering (ICSE)

作者： Ezzini, Saad Abualhaija, Sallam Arora, Chetan Sabetzadeh, Mehrdad Univ Luxembourg SnT Ctr Secur Reliabil & Trust Luxembourg Luxembourg Deakin Univ Geelong Australia Monash Univ Victoria Australia Univ Ottawa Sch Elect Engn & Comp Sci Ottawa ON Canada

ISBN: (纸本)9781665457019

By virtue of being prevalently written in natural language (NL), requirements are prone to various defects, e.g., inconsistency and incompleteness. As such, requirements are frequently subject to quality assurance processes. These processes, when carried out entirely manually, are tedious and may further overlook important quality issues due to time and budget pressures. In this paper, we propose QAssist - a question-answering (QA) approach that provides automated assistance to stakeholders, including requirements engineers, during the analysis of NL requirements. Posing a question and getting an instant answer is beneficial in various quality-assurance scenarios, e.g., incompleteness detection. Answering requirements-related questions automatically is challenging since the scope of the search for answers can go beyond the given requirements specification. To that end, QAssist provides support for mining external domain-knowledge resources. Our work is one of the first initiatives to bring together QA and external domain knowledge for addressing requirements engineering challenges. We evaluate QAssist on a dataset covering three application domains and containing a total of 387 question-answer pairs. We experiment with state-of-the-art QA methods, based primarily on recent large-scale language models. In our empirical study, QAssist localizes the answer to a question to three passages within the requirements specification and within the external domain-knowledge resource with an average recall of 90.1% and 96.5%, respectively. QAssist extracts the actual answer to the posed question with an average accuracy of 84.2%.

关键词： natural-language Requirements Question Answering (QA) language Models natural language processing (NLP) natural language Generation (NLG) BERT T5

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S-EVALUATOR: ENHANCE FACTUAL CONSISTENCY EVALUATOR WITH ADVERSARIAL DATA SYNTHESIZED BY LARGE language MODEL 49

S-EVALUATOR: ENHANCE FACTUAL CONSISTENCY EVALUATOR WITH ADVE...

引用

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

作者： Liu, Junnan Du, Wenlong Li, Qingquan Wang, Xuewei Zhou, Zhongjun Liu, Jin Ant Grp Hangzhou Peoples R China

ISBN: (纸本)9798350344868;9798350344851

With the rapid development of LLMs, the evaluation of factual consistency between source documents and generated texts plays a more crucial role in natural language generation (NLG). Recent methods usually suffer from low quality and insufficient quantity of training data. In this paper, we propose a method for synthesizing factual consistency data by harnessing the vast knowledge stored within large language models. The synthetic data produced through this approach demonstrates notable discriminative abilities and robustness for training the factual consistency evaluation model. We adopt experiments on two benchmark datasets (TRUE and SummaC) and our method achieves 3.5% and 4.6% relative improvement on AUC-ROC metric respectively.

关键词： factual consistency large language model evaluation

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DYNAMIC DATA SAMPLER FOR CROSS-language TRANSFER LEARNING IN LARGE language MODELS 49

DYNAMIC DATA SAMPLER FOR CROSS-LANGUAGE TRANSFER LEARNING IN...

引用

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

作者： Li, Yudong Feng, Yuhao Zhou, Wen Zhao, Zhe Shen, Linlin Hou, Cheng Hou, Xianxu Shenzhen Univ Sch Comp Sci & Software Engn Shenzhen Guangdong Peoples R China Tencent AI Lab Shenzhen Guangdong Peoples R China Wuhan Univ LIESMARS Wuhan Hubei Peoples R China Xian Jiaotong Liverpool Sch AI & Adv Comp Xian Shaanxi Peoples R China

ISBN: (纸本)9798350344868;9798350344851

Large language Models (LLMs) have gained significant attention in the field of natural language processing (NLP) due to their wide range of applications. However, training LLMs for languages other than English poses significant challenges, due to the difficulty in acquiring large-scale corpus and the requisite computing resources. In this paper, we propose ChatFlow, a cross-language transfer-based LLM, to address these challenges and train large Chinese language models in a cost-effective manner. We employ a mix of Chinese, English, and parallel corpus to continuously train the LLaMA2 model, aiming to align cross-language representations and facilitate the knowledge transfer specifically to the Chinese language model. In addition, we use a dynamic data sampler to progressively transition the model from unsupervised pre-training to supervised fine-tuning. Experimental results demonstrate that our approach accelerates model convergence and achieves superior performance. We evaluate ChatFlow on popular Chinese and English benchmarks, the results indicate that it outperforms other Chinese models post-trained on LLaMA-2-7B.

关键词： Large language model cross-language knowledge transfer

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An End-to-End Framework for Cybersecurity Taxonomy and Ontology Generation and Updating

An End-to-End Framework for Cybersecurity Taxonomy and Ontol...

引用

4th ieee Annual international conference on Cyber Security and Resilience (ieee CSR)

作者： Kougioumtzidou, Anna Papoutsis, Angelos Kavallieros, Dimatios Mavropoulos, Thanassis Tsikrika, Theodora Vrochidis, Stefanos Kompatsiaris, Ioannis CERTH Informat Technol Inst Thessaloniki Greece

ISBN: (纸本)9798350375367

Effective cyber-defense practices often require the use of structured knowledge representations, such as taxonomies and ontologies, to organise vast amounts of data and facilitate knowledge representation and reasoning. To this end, we present an Artificial Intelligence (AI)-assisted framework for the construction and update of cybersecurity taxonomies and ontologies. The proposed framework can be divided into three main phases: Taxonomy Construction, Ontology Construction, and Taxonomy/Ontology Update, each phase consisting of both information extraction and semantic knowledge representation components. For information extraction, we employ a variety of techniques originating from natural language processing (NLP), particularly Transformer Neural Networks. For constructing ontologies, we propose a conceptual ontology schema based on the STIX 2.1 standard for modeling information related to attacks, threats, and vulnerabilities, and use the Owlready2 Python library. Overall, our framework effectively builds cybersecurity taxonomies and ontologies and updates existing knowledge of both the generated and open-source taxonomies and ontologies.

关键词： ontologies taxonomies cybersecurity attacks vulnerabilities dynamic update large language models natural language processing artificial intelligence

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JaFIn: Japanese Financial Instruction Dataset

JaFIn: Japanese Financial Instruction Dataset

引用

2024 conference on Computational Intelligence for Financial engineering and Economics

作者： Tanabe, Kota Suzuki, Masahiro Sakaji, Hiroki Noda, Itsuki Hokkaido Univ Fac Informat Sci & Technol Sapporo Hokkaido Japan Univ Tokyo Sch Engn Tokyo Japan

ISBN: (纸本)9798350354843;9798350354836

We construct an instruction dataset for the large language model (LLM) in the Japanese finance domain. Domain adaptation of language models, including LLMs, is receiving more attention as language models become more popular. This study demonstrates the effectiveness of domain adaptation through instruction tuning. To achieve this, we propose an instruction tuning data in Japanese called JaFIn, the Japanese Financial Instruction Dataset. JaFIn is manually constructed based on multiple data sources, including Japanese government websites, which provide extensive financial knowledge. We then utilize JaFIn to apply instruction tuning for several LLMs, demonstrating that our models specialized in finance have better domain adaptability than the original models. The financial-specialized LLMs created were evaluated using a quantitative Japanese financial benchmark and qualitative response comparisons, showing improved performance over the originals.

关键词： Financial natural language processing Large language Model Instruction Dataset Instruction Tuning Domain Adaptation

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knowledge-AWARE PROMPT LEARNING FRAMEWORK FOR KOREAN-CHINESE MICROBLOG SENTIMENT ANALYSIS 49

KNOWLEDGE-AWARE PROMPT LEARNING FRAMEWORK FOR KOREAN-CHINESE...

引用

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

作者： Yang, Xinyu Wang, Hengxuan Jin, Hulling Zhang, Zhenguo Yuan, Xiaojie Nankai Univ Coll Comp Sci Tianjin Peoples R China Yanbian Univ Dept Comp Sci & Technol Yanbian Peoples R China Yanbian Univ Integrat Coll Dept Computat Linguist Yanbian Peoples R China

ISBN: (纸本)9798350344868;9798350344851

The Korean-Chinese language spoken by the Chinese Koreans, a cross-border ethnic group in China, has distinct linguistic characteristics compared to the standard Korean. Despite the increasing presence of Korean-Chinese microblogs on the Sina Microblog Platform, sentiment analysis in this language is still in its early stages. To bridge this gap, we construct a Korean-Chinese Microblog Sentiment Analysis (KCMSA) dataset. To maximize the benefits of Pre-trained language Models, we propose the knowledge-Aware Prompt Learning Framework (KAP). Our framework utilizes prompt learning and integrates Korean sentiment knowledge base to enhance accuracy, and leverages multiple refinement operations to reduce the introduced noise. Baseline evaluations and experiments over existing Korean short-text social platform datasets demonstrate the superiority of KAP.

关键词： Korean-Chinese language processing Prompt Learning Sentiment Analysis

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Querying knowledge Graphs in Greek language 17

Querying Knowledge Graphs in Greek Language

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17th international conference on PErvasive Technologies Related to Assistive Environments (PETRA)

作者： Tsampos, Ioannis Marakakis, Emmanouil Hellen Mediterranean Univ Dept Elect & Comp Engn Khania Greece

ISBN: (纸本)9798400717604

We present a method for querying knowledge graphs through natural language, emphasizing its application to the Greek language. It integrates NLP techniques with the capabilities of graph databases to enable seamless interaction with knowledge graphs through natural language queries. Upon receiving a user's question in Greek language, we use linguistic analysis tools to convert it into a graph structure by employing predefined rules and adhering to a graph database schema. This methodology enables the handling of different question types and the efficient extraction of relations, ensuring accurate mapping of linguistic structures to database queries. The representation of a question as a knowledge graph enables its direct translation to a Cypher query, facilitating the extraction of related answers. The approach can handle complex questions and is language independent.

关键词： knowledge Graph Construction natural language processing knowledge Representation Question Answering Greek language Graph Databases Query languages

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