CodeXGLUE is a benchmark dataset and open challenge for code intelligence. It includes a collection of code intelligence tasks and a platform for model evaluation and comparison. CodeXGLUE stands for General Language Understanding Evaluation benchmark for CODE. It includes 14 datasets for 10 diversified code intelligence tasks covering the following scenarios:
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WMT 2020 is a collection of datasets used in shared tasks of the Fifth Conference on Machine Translation. The conference builds on a series of annual workshops and conferences on Statistical Machine Translation.
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The IWSLT 2015 Evaluation Campaign featured three tracks: automatic speech recognition (ASR), spoken language translation (SLT), and machine translation (MT). For ASR we offered two tasks, on English and German, while for SLT and MT a number of tasks were proposed, involving English, German, French, Chinese, Czech, Thai, and Vietnamese. All tracks involved the transcription or translation of TED talks, either made available by the official TED website or by other TEDx events. A notable change with respect to previous evaluations was the use of unsegmented speech in the SLT track in order to better fit a real application scenario.
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