A Syntax-Guided Neural Model for Natural Language Interfaces to Databases

Brad, Florin; Iacob, Radu Cristian Alexandru; Hosu, Ionel; Ruşeţi, Ştefan; Rebedea, Traian

doi:10.1109/ictai.2018.00043

Cited by 2 publications

(5 citation statements)

References 10 publications

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“…In their design, they introduce new models for translation, seamlessly merging deep learning with traditional techniques for database analysis and developing new techniques to allow the neural network to reject queries irrelevant to the content of the target database, and recommend that candidate queries be translated back into a tuned natural language. According to [21], the slow development of Natural Language Interfaces for Databases (NLIDB) stems from linguistic issues (such as language ambiguity) as well as domain portability. Brad et al [21]assert that there is a demand for non-expert users to make queries to relational databases using their natural language instead of utilizing specified attribute values for the database domain.…”

Section: Natural Language Processing (Nlp)mentioning

confidence: 99%

“…According to [21], the slow development of Natural Language Interfaces for Databases (NLIDB) stems from linguistic issues (such as language ambiguity) as well as domain portability. Brad et al [21]assert that there is a demand for non-expert users to make queries to relational databases using their natural language instead of utilizing specified attribute values for the database domain. According to [22], the use of natural language instead of SQL-based queries enabled the development of NLIDB, a new type of processing that represents an approach to developing intelligent database systems aimed at improving the performance of flexible queries in databases.…”

Section: Natural Language Processing (Nlp)mentioning

confidence: 99%

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Explicit Knowledge Database Interface Model System Based on Natural Language Processing Techniques and Immersive Technologies

Alfaro,

Rivera,

Herrera

et al. 2023

IJACSA

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This work is focused on the proposal and development of an interface system model, based on natural language processing, immersive technologies and natural user interfaces, for the interaction with Explicit Knowledge databases. Five phases were proposed: The user testing characterization, the establishment of the state of the art and the theoretical foundation, the design and development of software, the system implementation and the functional tests and evaluation of the usability of the interface model. In order to establish the user testing characterization and the corresponding theoretical framework, the expert guide on Knowledge Management and Virtual Reality was followed, based on the approach of Usability and Computer Ergonomics compatible with the ISO 9241 standard. The traditional interfaces and the proposal in this work were evaluated for each of the metrics defined by the ISO 9241 standard, considering the dimensions of effectiveness, efficiency and satisfaction. The statistical test of "T-Student" established that there is enough evidence to confirm the existence of the following significant differences: Effectiveness is lower using the proposed interface model; efficiency and satisfaction is higher using the proposed interface model. Based on the conducted tests, it can be established that the proposed interface model is superior to the traditional interface in terms of the "Efficiency and Satisfaction" dimensions and inferior in terms of "Effectiveness." Consequently, it can be concluded that the scientific article exploration model using VR and NLP is superior to the traditional model.

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Section: Natural Language Processing (Nlp)mentioning

confidence: 99%

Section: Natural Language Processing (Nlp)mentioning

confidence: 99%

Explicit Knowledge Database Interface Model System Based on Natural Language Processing Techniques and Immersive Technologies

Alfaro,

Rivera,

Herrera

et al. 2023

IJACSA

View full text Add to dashboard Cite

show abstract

“…Ambiguity. Another problem that blocks the research process is language ambiguity [ 31 ]. After a long-term NL evolution, people tend to express their thoughts relatively concisely, such as with fewer words and shorter phrases.…”

Section: Insights Gained From Nlscg Research Backlogmentioning

confidence: 99%

“…The dataset is one of the essential factors determining the upper bound of the model performance to some extent. The abundance of high-quality labeled data is critical for effectively training supervised models [ 31 , 51 ]. However, manually annotating NL utterances with their corresponding SC is expensive, cumbersome, and time consuming [ 10 , 38 , 52 , 53 ].…”

Section: Insights Gained From Nlscg Research Backlogmentioning

confidence: 99%

“…The well formedness, type-sensitiveness, executability [ 4 ], and semantics of source code create the most difficulties. Many studies [ 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 , 53 , 54 , 55 , 56 , 57 , 58 , ...…”

Section: Introductionmentioning

confidence: 99%

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Recent Advances in Intelligent Source Code Generation: A Survey on Natural Language Based Studies

Yang

Liu

Yin

2021

Entropy

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Source Code Generation (SCG) is a prevalent research field in the automation software engineering sector that maps specific descriptions to various sorts of executable code. Along with the numerous intensive studies, diverse SCG types that integrate different scenarios and contexts continue to emerge. As the ultimate purpose of SCG, Natural Language-based Source Code Generation (NLSCG) is growing into an attractive and challenging field, as the expressibility and extremely high abstraction of the input end. The booming large-scale dataset generated by open-source code repositories and Q&A resources, the innovation of machine learning algorithms, and the development of computing capacity make the NLSCG field promising and give more opportunities to the model implementation and perfection. Besides, we observed an increasing interest stream of NLSCG relevant studies recently, presenting quite various technical schools. However, many studies are bound to specific datasets with customization issues, producing occasional successful solutions with tentative technical methods. There is no systematic study to explore and promote the further development of this field. We carried out a systematic literature survey and tool research to find potential improvement directions. First, we position the role of NLSCG among various SCG genres, and specify the generation context empirically via software development domain knowledge and programming experiences; second, we explore the selected studies collected by a thoughtfully designed snowballing process, clarify the NLSCG field and understand the NLSCG problem, which lays a foundation for our subsequent investigation. Third, we model the research problems from technical focus and adaptive challenges, and elaborate insights gained from the NLSCG research backlog. Finally, we summarize the latest technology landscape over the transformation model and depict the critical tactics used in the essential components and their correlations. This research addresses the challenges of bridging the gap between natural language processing and source code analytics, outlines different dimensions of NLSCG research concerns and technical utilities, and shows a bounded technical context of NLSCG to facilitate more future studies in this promising area.

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A Syntax-Guided Neural Model for Natural Language Interfaces to Databases

Cited by 2 publications

References 10 publications

Explicit Knowledge Database Interface Model System Based on Natural Language Processing Techniques and Immersive Technologies

Explicit Knowledge Database Interface Model System Based on Natural Language Processing Techniques and Immersive Technologies

Recent Advances in Intelligent Source Code Generation: A Survey on Natural Language Based Studies

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