TITLE: XLCoST: A Benchmark Dataset for Cross-lingual Code Intelligence 

ABSTRACT: Recent advances in machine learning have significantly improved the
understanding of source code data and achieved good performance on a number of
downstream tasks. Open source repositories like GitHub enable this process with
rich unlabeled code data. However, the lack of high quality labeled data has
largely hindered the progress of several code related tasks, such as program
translation, summarization, synthesis, and code search. This paper introduces
XLCoST, Cross-Lingual Code SnippeT dataset, a new benchmark dataset for
cross-lingual code intelligence. Our dataset contains fine-grained parallel
data from 8 languages (7 commonly used programming languages and English), and
supports 10 cross-lingual code tasks. To the best of our knowledge, it is the
largest parallel dataset for source code both in terms of size and the number
of languages. We also provide the performance of several state-of-the-art
baseline models for each task. We believe this new dataset can be a valuable
asset for the research community and facilitate the development and validation
of new methods for cross-lingual code intelligence.
Recent advances in machine learning have significantly improved the
understanding of source code data and achieved good performance on a number of
downstream tasks. Open source repositories like GitHub enable this process with
rich unlabeled code data. However, the lack of high quality labeled data has
largely hindered the progress of several code related tasks, such as program
translation, summarization, synthesis, and code search. This paper introduces
XLCoST, Cross-Lingual Code SnippeT dataset, a new benchmark dataset for
cross-lingual code intelligence. Our dataset contains fine-grained parallel
data from 8 languages (7 commonly used programming languages and English), and
supports 10 cross-lingual code tasks. To the best of our knowledge, it is the
largest parallel dataset for source code both in terms of size and the number
of languages. We also provide the performance of several state-of-the-art
baseline models for each task. We believe this new dataset can be a valuable
asset for the research community and facilitate the development and validation
of new methods for cross-lingual code intelligence.
XLCoST: A Benchmark Dataset for Cross-lingual Code Intelligence
http://arxiv.org/abs/2206.08474v1