Integrating Tree Path in Transformer for Code RepresentationDownload PDF

May 21, 2021 (edited Nov 05, 2021)NeurIPS 2021 PosterReaders: Everyone
  • Keywords: machine learning for code, code summarization
  • TL;DR: Integrating path encodings as inductive bias for self-attention improves learning for source code.
  • Abstract: Learning distributed representation of source code requires modelling its syntax and semantics. Recent state-of-the-art models leverage highly structured source code representations, such as the syntax trees and paths therein. In this paper, we investigate two representative path encoding methods shown in previous research work and integrate them into the attention module of Transformer. We draw inspiration from the ideas of positional encoding and modify them to incorporate these path encoding. Specifically, we encode both the pairwise path between tokens of source code and the path from the leaf node to the tree root for each token in the syntax tree. We explore the interaction between these two kinds of paths by integrating them into the unified Transformer framework. The detailed empirical study for path encoding methods also leads to our novel state-of-the-art representation model TPTrans, which finally outperforms strong baselines. Extensive experiments and ablation studies on code summarization across four different languages demonstrate the effectiveness of our approaches. We release our code at \url{https://github.com/AwdHanPeng/TPTrans}.
  • Supplementary Material: pdf
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  • Code: https://github.com/AwdHanPeng/TPTrans
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