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Code Completion using Neural Attention and Byte Pair Encoding

2020-04-14 · Youri Arkesteijn, Nikhil Saldanha, Bastijn Kostense

In this paper, we aim to do code completion based on implementing a Neural Network from Li et. al.. Our contribution is that we use an encoding that is in-between character and word encoding called Byte Pair Encoding (BPE). We use this on the source code files treating them as natural text without first going through the abstract syntax tree (AST). We have implemented two models: an attention-enhanced LSTM and a pointer network, where the pointer network was originally introduced to solve out of vocabulary problems. We are interested to see if BPE can replace the need for the pointer network for code completion.

📄 PDF Abstract BibTeX arXiv:2004.06343

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Code Completion

Methods 이 논문이 사용한 방법론

Sigmoid Activation 설명 없음
Tanh Activation 설명 없음
BPE Byte Pair Encoding, or BPE, is a subword segmentation algorithm that encodes rare and unknown words as sequences of subword units. The intuition is that various word…
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Softmax The Softmax output function transforms a previous layer's output into a vector of probabilities. It is commonly used for multiclass classification. Given an input vector $x$…
Pointer Network Pointer Networks tackle problems where input and output data are sequential data, but can't be solved by seq2seq type models because discrete categories of output elements…
LSTM An LSTM is a type of recurrent neural network that addresses the vanishing gradient problem in vanilla…

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