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Participatory Design in Digital Language Documentation: A Web Platform Approach
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Quality Assurance of Generative Dialog Models in an Evolving Conversational Agent Used for Swedish Language Practice ...
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SPT-Code: Sequence-to-Sequence Pre-Training for Learning Source Code Representations ...
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Software interactivo como herramienta de aprendizaje para niños con discapacidad auditiva en la U.E. "Especial Maturín", estado Monagas ...
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Software interactivo como herramienta de aprendizaje para niños con discapacidad auditiva en la U.E. "Especial Maturín", estado Monagas ...
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Eine agentenbasierte Architektur für Programmierung mit gesprochener Sprache ...
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Grenzüberschreitendes Textmining von Historischen Zeitungen - Das impresso-Projekt zwischen Text- und Bildverarbeitung, Design und Geschichtswissenschaft ...
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Grenzüberschreitendes Textmining von Historischen Zeitungen - Das impresso-Projekt zwischen Text- und Bildverarbeitung, Design und Geschichtswissenschaft ...
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PACSafe: Leveraging ARM Pointer Authentication for Memory Safety in C/C++ ...
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Quickstrom: Property Based Acceptance Testing with LTL Specifications ...
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POLYCRUISE: A Cross-Language Dynamic Information Flow Analysis ...
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POLYCRUISE: A Cross-Language Dynamic Information Flow Analysis ...
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MSCCD: Grammar Pluggable Clone Detection Based on ANTLR Parser Generation ...
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Classification of Natural Language Processing Techniques for Requirements Engineering ...
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Conceptual Modeling of Events Based on One-Category Ontology ...
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Accelerating Code Search with Deep Hashing and Code Classification ...
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Abstract:
Code search is to search reusable code snippets from source code corpus based on natural languages queries. Deep learning-based methods of code search have shown promising results. However, previous methods focus on retrieval accuracy but lacked attention to the efficiency of the retrieval process. We propose a novel method CoSHC to accelerate code search with deep hashing and code classification, aiming to perform an efficient code search without sacrificing too much accuracy. To evaluate the effectiveness of CoSHC, we apply our method to five code search models. Extensive experimental results indicate that compared with previous code search baselines, CoSHC can save more than 90% of retrieval time meanwhile preserving at least 99% of retrieval accuracy. ... : Accepted to 60th Annual Meeting of the Association for Computational Linguistics (ACL 2022) ...
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Keyword:
Artificial Intelligence cs.AI; FOS Computer and information sciences; Software Engineering cs.SE
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URL: https://dx.doi.org/10.48550/arxiv.2203.15287 https://arxiv.org/abs/2203.15287
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