Papers › Code Summarization with Structure-induced Transformer

Code Summarization with Structure-induced Transformer

29 Dec 2020Findings (ACL) 2021 8arXiv:2012.14710archive 2025-07-28

Hongqiu Wu, Hai Zhao, Min Zhang

Code summarization (CS) is becoming a promising area in recent language understanding, which aims to generate sensible human language automatically for programming language in the format of source code, serving in the most convenience of programmer developing. It is well known that programming languages are highly structured. Thus previous works attempt to apply structure-based traversal (SBT) or non-sequential models like Tree-LSTM and graph neural network (GNN) to learn structural program semantics. However, it is surprising that incorporating SBT into advanced encoder like Transformer instead of LSTM has been shown no performance gain, which lets GNN become the only rest means modeling such necessary structural clue in source code. To release such inconvenience, we propose structure-induced Transformer, which encodes sequential code inputs with multi-view structural clues in terms of a newly-proposed structure-induced self-attention mechanism. Extensive experiments show that our proposed structure-induced Transformer helps achieve new state-of-the-art results on benchmarks.

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Code Summarization Graph Neural Network Natural Language Understanding Source Code Summarization

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Absolute Position Encodings Adam Attention BPE Dense Connections Dropout Graph Neural Network LSTM Label Smoothing Layer Normalization Linear Layer Multi-Head Attention Position-Wise Feed-Forward Layer Residual Connection Sigmoid Activation Softmax Tanh Activation Transformer

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