Download PDFOpen PDF in browserDeep Learning On Code with an Unbounded VocabularyEasyChair Preprint 46611 pages•Date: August 29, 2018AbstractA major challenge when using techniques from Natural Language Processing for supervised learning on computer program source code is that many words in code are neologisms. Reasoning over such an unbounded vocabulary is not something NLP methods are typically suited for. We introduce a deep model that contends with an unbounded vocabulary (at training or test time) by embedding new words as nodes in a graph as they are encountered and processing the graph with a Graph Neural Network. Keyphrases: Graph Neural Network, Graph Neural Networks, Learning Representation, Natural Language Processing, abstract syntax tree, ast augmented ast, augmented ast, control flow, deep learning, fixed vocabulary, machine learning, neural network, source code, unbounded vocabulary, variable naming task
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