MultiCoder: Multi-Programming-Lingual Pre-Training for Low-Resource Code Completion

• URL: http://arxiv.org/abs/2212.09666v1
• Date: Mon, 19 Dec 2022 17:50:05 GMT
• Title: MultiCoder: Multi-Programming-Lingual Pre-Training for Low-Resource Code Completion
• Authors: Zi Gong, Yinpeng Guo, Pingyi Zhou, Cuiyun Gao, Yasheng Wang, Zenglin Xu
• Abstract summary: We propose the MultiCoder to enhance the low-resource code completion via MultiPL pre-training and MultiPL Mixture-of-Experts layers. We also propose a novel PL-level MoE routing strategy (PL-MoE) for improving the code completion on all PLs.
• Score: 21.100570496144694
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Code completion is a valuable topic in both academia and industry. Recently, large-scale mono-programming-lingual (MonoPL) pre-training models have been proposed to boost the performance of code completion. However, the code completion on low-resource programming languages (PL) is difficult for the data-driven paradigm, while there are plenty of developers using low-resource PLs. On the other hand, there are few studies exploring the effects of multi-programming-lingual (MultiPL) pre-training for the code completion, especially the impact on low-resource programming languages. To this end, we propose the MultiCoder to enhance the low-resource code completion via MultiPL pre-training and MultiPL Mixture-of-Experts (MoE) layers. We further propose a novel PL-level MoE routing strategy (PL-MoE) for improving the code completion on all PLs. Experimental results on CodeXGLUE and MultiCC demonstrate that 1) the proposed MultiCoder significantly outperforms the MonoPL baselines on low-resource programming languages, and 2) the PL-MoE module further boosts the performance on six programming languages. In addition, we analyze the effects of the proposed method in details and explore the effectiveness of our method in a variety of scenarios.

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