Papers by Colin Clement
Long-Range Modeling of Source Code Files with eWASH: Extended Window Access by Syntax Hierarchy (2021.emnlp-main)
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Colin Clement, Shuai Lu, Xiaoyu Liu, Michele Tufano, Dawn Drain, Nan Duan, Neel Sundaresan, Alexey Svyatkovskiy
| Challenge: | Statistical language modeling and translation with transformers have found many successful applications in program understanding and generation tasks. |
| Approach: | They propose an architecture-independent approach for leveraging syntactic hierarchies of source code . they use syntax trees to extract syntak hierarchical structures and integrate them into context window . |
| Outcome: | The proposed approach achieves state-of-the-art in code completion and summarization for Python in the CodeXGLUE benchmark. |
Program Translation via Code Distillation (2023.emnlp-main)
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| Challenge: | Software version migration and program translation are costly parts of the lifecycle of large codebases. |
| Approach: | They propose a model that captures semantic and structural equivalence of code in a language agnostic intermediate representation. |
| Outcome: | The proposed model achieves state-of-the-art performance on CodeXGLUE and TransCoder GeeksForGeeks translation benchmarks. |
SUT: Active Defects Probing for Transcompiler Models (2023.emnlp-main)
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Mengnan Qi, Yufan Huang, Maoquan Wang, Yongqiang Yao, Zihan Liu, Bin Gu, Colin Clement, Neel Sundaresan
| Challenge: | Existing datasets are often criticized for their lack of granularity, which can mask deficiencies in basic syntactic elements that humans care about. |
| Approach: | They propose a new program translation metrics that address basic syntax errors . they propose BLUE, CodeBLUE and computation accuracy metrics which address these errors based on a highly interpretable evaluation harness. |
| Outcome: | The proposed model passes the unit tests with a 26.15% pass rate compared to previous models . |
PyMT5: multi-mode translation of natural language and Python code with transformers (2020.emnlp-main)
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| Challenge: | Using Python method text-to-text transfer transformers, developers can easily model source code and natural language. |
| Approach: | They propose a Python method text-to-text transfer transformer that can translate between all pairs of Python method feature combinations. |
| Outcome: | The proposed model outperforms similar-sized auto-regressive language models on a large-scale parallel corpus of 26 million methods and 7.7 million method-docstring pairs on the CodeSearchNet test set. |