The field of programming has a diversity of paradigms that are used according
to the working framework. While current neural code generation methods are able
to learn and generate code directly from text, we believe that this approach is
not optimal for certain code tasks, particularly the generation of classes in
an object-oriented project. Specifically, we use natural language processing
techniques to extract structured information from requirements descriptions, in
order to automate the generation of CRUD (Create, Read, Update, Delete) class
code. To facilitate this process, we introduce a pipeline for extracting entity
and relation information, as well as a representation called an "Entity Tree"
to model this information. We also create a dataset to evaluate the
effectiveness of our approach.

本文提出了一种使用自然语言处理技术来从需求描述中提取结构化信息，使得可以自动完成 CRUD 类代码的生成的方法，并介绍了相应的实体识别、关系提取和实体树模型技术，以及评估这种方法效果的数据集。

实体标识符：一种基于自然文本解析的实体关系抽取框架

Entity Identifier: A Natural Text Parsing-based Framework For Entity  Relation Extraction

Code generation aims to automatically generate a piece of code given an input
natural language utterance. Currently, among dominant models, it is treated as
a sequence-to-tree task, where a decoder outputs a sequence of actions
corresponding to the pre-order traversal of an Abstract Syntax Tree. However,
such a decoder only exploits the preorder traversal based preceding actions,
which are insufficient to ensure correct action predictions. In this paper, we
first throughly analyze the context modeling difference between neural code
generation models with different traversals based decodings (preorder traversal
vs breadth-first traversal), and then propose to introduce a mutual learning
framework to jointly train these models. Under this framework, we continuously
enhance both two models via mutual distillation, which involves synchronous
executions of two one-to-one knowledge transfers at each training step. More
specifically, we alternately choose one model as the student and the other as
its teacher, and require the student to fit the training data and the action
prediction distributions of its teacher. By doing so, both models can fully
absorb the knowledge from each other and thus could be improved simultaneously.
Experimental results and in-depth analysis on several benchmark datasets
demonstrate the effectiveness of our approach. We release our code at
this https URL

本文提出了一种互联互通学习框架，以解决在神经代码生成模型中不同遍历方法的编码差异，并利用两种模型的互相蒸馏技术来提高模型的准确性。通过实验数据的支持，该模型在几个基准测试数据集上表现出很高的效果。