Data Ambiguity Profiling for the Generation of Training Examples

Veltri, Enzo; Badaro, Gilbert; Saeed, Mohammed; Papotti, Paolo

doi:10.1109/icde55515.2023.00041

Cited by 6 publications

(2 citation statements)

References 34 publications

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“…Additionally, efforts are made to minimize ambiguities in healthcare regulations related to software development, emphasizing the role of Business Process Reengineering techniques [16]. A study introduces a framework to identify Ambiguity in user stories, recognizing that Ambiguity in this context is rooted in linguistic and cognitive problems and also addresses why cognitive factors trigger Ambiguity and how to visualize Ambiguity in user stories, setting the stage for an experiment involving advanced students to test the effectiveness of the framework [17]. The Quality User Story (QUS) framework, consisting of 13 quality criteria for user stories, and the Automatic Quality User Story Artisan (AQUSA) software tool utilizes natural language processing (NLP) techniques to detect and suggest remedies for quality defects in user stories, with an evaluation analyzing 1023 user stories from 18 software companies.…”

Section: Complexity In Agile User Storiesmentioning

confidence: 99%

Ambiguity in Business Process Reengineering: A Comprehensive Review of Trends, Challenges and Future Prospects.

Alam

2024

Preprint

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This document presents a comprehensive Systematic Literature Review (SLR) on the critical topic of Ambiguity In Business Process Reengineering (BPR). The review meticulously analyzes a range of methodologies, identifies prevalent challenges, and outlines potential avenues for future research in this domain. Through a thorough examination of 60 papers published between 2000 and 2023, this SLR illuminates evolving trends in Ambiguity Techniques, emphasizing the paramount importance of clear Business Process Reengineering in software development. By exploring the synergies between human expertise and computational methods, the review evaluates the strengths and weaknesses of various approaches, particularly their implications for collaborative efforts between Business Process Reengineering (BPR) engineers and testers. Notably, the study explores novel approaches aimed at problem-solving and preventing future challenges. Furthermore, it explores the promising integration of Artificial Intelligence and Machine learning with contextual awareness and adaptive learning models as a strategic means to address current limitations in unclear meaning. This research serves as a foundational contribution to advancing the field of BPR, with a specific focus on optimizing software development and delivery efficiency and effectiveness.

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Section: Complexity In Agile User Storiesmentioning

confidence: 99%

Ambiguity in Business Process Reengineering: A Comprehensive Review of Trends, Challenges and Future Prospects.

Alam

2024

Preprint

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“…While these works share some ideas with our approach, they cannot consume tables as input. One work focuses on the generation of ambiguous examples by profiling input relations for a new kind of metadata and in terms of example variety they only focus on look-up claims [50,51]. Semantic Parsing.…”

Section: Related Workmentioning

confidence: 99%

Generation of Training Examples for Tabular Natural Language Inference

Bussotti,

Veltri,

Santoro

et al. 2023

Proc. ACM Manag. Data

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Tabular data is becoming increasingly important in Natural Language Processing (NLP) tasks, such as Tabular Natural Language Inference (TNLI). Given a table and a hypothesis expressed in NL text, the goal is to assess if the former structured data supports or refutes the latter. In this work, we focus on the role played by the annotated data in training the inference model. We introduce a system, Tenet, for the automatic augmentation and generation of training examples for TNLI. Given the tables, existing approaches are either based on human annotators, and thus expensive, or on methods that produce simple examples that lack data variety and complex reasoning. Instead, our approach is built around the intuition that SQL queries are the right tool to achieve variety in the generated examples, both in terms of data variety and reasoning complexity. The first is achieved by evidence-queries that identify cell values over tables according to different data patterns. Once the data for the example is identified, semantic-queries describe the different ways such data can be identified with standard SQL clauses. These rich descriptions are then verbalized as text to create the annotated examples for the TNLI task. The same approach is also extended to create counterfactual examples, i.e., examples where the hypothesis is false, with a method based on injecting errors in the original (clean) table. For all steps, we introduce generic generation algorithms that take as input only the tables. For our experimental study, we use three datasets from the TNLI literature and two crafted by us on more complex tables. Tenet generates human-like examples, which lead to the effective training of several inference models with results comparable to those obtained by training the same models with manually-written examples.

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Large Language Models as Storage for SQL Querying

Papotti

2024

2024 IEEE 40th International Conference on Data Engineering (ICDE)

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Data Ambiguity Profiling for the Generation of Training Examples

Cited by 6 publications

References 34 publications

Ambiguity in Business Process Reengineering: A Comprehensive Review of Trends, Challenges and Future Prospects.

Ambiguity in Business Process Reengineering: A Comprehensive Review of Trends, Challenges and Future Prospects.

Generation of Training Examples for Tabular Natural Language Inference

Large Language Models as Storage for SQL Querying

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