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 . |
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Curious Case of Language Generation Evaluation Metrics: A Cautionary Tale (2020.coling-main)
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