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- W4386576744 abstract "While there has been a recent burgeoning of applications at the intersection of natural and programming languages, such as code generation and code summarization, these applications are usually English-centric. This creates a barrier for program developers who are not proficient in English. To mitigate this gap in technology development across languages, we propose a multilingual dataset, MCoNaLa, to benchmark code generation from natural language commands extending beyond English. Modeled off of the methodology from the English Code/Natural Language Challenge (CoNaLa) dataset, we annotated a total of 896 NL-Code pairs in three languages: Spanish, Japanese, and Russian. We present a systematic evaluation on MCoNaLa by testing state-of-the-art code generation systems. Although the difficulties vary across three languages, all systems lag significantly behind their English counterparts, revealing the challenges in adapting code generation to new languages." @default.
- W4386576744 created "2023-09-10" @default.
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- W4386576744 date "2023-01-01" @default.
- W4386576744 modified "2023-10-16" @default.
- W4386576744 title "MCoNaLa: A Benchmark for Code Generation from Multiple Natural Languages" @default.
- W4386576744 doi "https://doi.org/10.18653/v1/2023.findings-eacl.20" @default.
- W4386576744 hasPublicationYear "2023" @default.
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