Recognizing software entities such as library names from free-form text is
essential to enable many software engineering (SE) technologies, such as
traceability link recovery, automated documentation, and API recommendation.
While many approaches have been proposed to address this problem, they suffer
from small entity vocabularies or noisy training data, hindering their ability
to recognize software entities mentioned in sophisticated narratives. To
address this challenge, we leverage the Wikipedia taxonomy to develop a
comprehensive entity lexicon with 79K unique software entities in 12
fine-grained types, as well as a large labeled dataset of over 1.7M sentences.
Then, we propose self-regularization, a noise-robust learning approach, to the
training of our software entity recognition (SER) model by accounting for many
dropouts. Results show that models trained with self-regularization outperform
both their vanilla counterparts and state-of-the-art approaches on our
Wikipedia benchmark and two Stack Overflow benchmarks. We release our models,
data, and code for future research.

通过利用维基百科分类法，建立一个拥有 79K 个软件实体和 12 种详细类型的全面实体词典以及一个包含 1.7M 个句子的大型标注数据集，我们提出了自我正则化的软件实体识别（SER）模型训练方法，能够克服语料库中噪音和训练数据不足的问题，并在维基百科和两个 Stack Overflow 基准测试中展现了优于基准模型和现有方法的性能。我们公开了我们的模型、数据和代码，以供未来研究使用。

噪音鲁棒学习的软件实体识别

Software Entity Recognition with Noise-Robust Learning

Automated documentation of programming source code and automated code
generation from natural language are challenging tasks of both practical and
scientific interest. Progress in these areas has been limited by the low
availability of parallel corpora of code and natural language descriptions,
which tend to be small and constrained to specific domains.
In this work we introduce a large and diverse parallel corpus of a hundred
thousands Python functions with their documentation strings ("docstrings")
generated by scraping open source repositories on GitHub. We describe baseline
results for the code documentation and code generation tasks obtained by neural
machine translation. We also experiment with data augmentation techniques to
further increase the amount of training data.
We release our datasets and processing scripts in order to stimulate research
in these areas.

本论文介绍了一个由十万个 Python 函数及其文档字符串（docstrings）组成的大型多样并行语料库，用于自然语言生成代码和代码文档生成任务的基线结果是经过神经机器翻译获得的。