Exploring Dynamic Selection of Branch Expansion Orders for Code Generation

• URL: http://arxiv.org/abs/2106.00261v1
• Date: Tue, 1 Jun 2021 06:52:41 GMT
• Title: Exploring Dynamic Selection of Branch Expansion Orders for Code Generation
• Authors: Hui Jiang, Chulun Zhou, Fandong Meng, Biao Zhang, Jie Zhou, Degen Huang, Qingqiang Wu, Jinsong Su
• Abstract summary: We equip the Seq2Tree model with a context-based Branch Selector. The selector is able to dynamically determine optimal expansion orders of branches for multi-branch nodes. Experimental results and in-depth analysis on several commonly-used datasets demonstrate the effectiveness and generality of our approach.
• Score: 43.027059412639
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Due to the great potential in facilitating software development, code generation has attracted increasing attention recently. Generally, dominant models are Seq2Tree models, which convert the input natural language description into a sequence of tree-construction actions corresponding to the pre-order traversal of an Abstract Syntax Tree (AST). However, such a traversal order may not be suitable for handling all multi-branch nodes. In this paper, we propose to equip the Seq2Tree model with a context-based Branch Selector, which is able to dynamically determine optimal expansion orders of branches for multi-branch nodes. Particularly, since the selection of expansion orders is a non-differentiable multi-step operation, we optimize the selector through reinforcement learning, and formulate the reward function as the difference of model losses obtained through different expansion orders. Experimental results and in-depth analysis on several commonly-used datasets demonstrate the effectiveness and generality of our approach. We have released our code at https://github.com/DeepLearnXMU/CG-RL.

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