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Open-domain code generation aims to generate code in a general-purpose programming language (such as Python) from natural language (NL) intents.
This paper presents a neural network code generator (NNCG) that generates from a trained CNN a plain ANSI C code file that encapsulates the inference in single a function.
Program synthesis of general-purpose source code from natural language specifications is challenging due to the need to reason about high-level patterns in the target program and low-level implementation details at the same time.
Code summarization (CS) and code generation (CG) are two crucial tasks in the field of automatic software development.
TreeGen outperformed the previous state-of-the-art approach by 4. 5 percentage points on HearthStone, and achieved the best accuracy among neural network-based approaches on ATIS (89. 1%) and GEO (89. 6%).
Interactive programming with interleaved code snippet cells and natural language markdown is recently gaining popularity in the form of Jupyter notebooks, which accelerate prototyping and collaboration.
In this paper, we introduce the AML concept models for representing OWL complex classes in AutomationML, and present algorithms for the bidirectional translation between OWL complex classes and their corresponding AML concept models.
Semantic parsing aims to transform natural language (NL) utterances into formal meaning representations (MRs), whereas an NL generator achieves the reverse: producing a NL description for some given MRs.