We implement a divide-and-concur iterative projection approach to
context-free grammar inference. Unlike most state-of-the-art models of natural
language processing, our method requires a relatively small number of discrete
parameters, making the inferred grammar directly interpretable -- one can read
off from a solution how to construct grammatically valid sentences. Another
advantage of our approach is the ability to infer meaningful grammatical rules
from just a few sentences, compared to the hundreds of gigabytes of training
data many other models employ. We demonstrate several ways of applying our
approach: classifying words and inferring a grammar from scratch, taking an
existing grammar and refining its categories and rules, and taking an existing
grammar and expanding its lexicon as it encounters new words in new data.

本研究提出了一种分而治之的迭代投影方法，用于上下文无关文法推断。相对于大多数自然语言处理模型，我们的方法只需要相对较少的离散参数，使得学习到的文法可以直接解释。此外，我们的方法可以从极少的语料中推断出有意义的语法规则。我们演示了几种应用该方法的方式：从头开始分类词汇和推断语法，使用现有文法并细化其类别和规则，以及在遇到新数据中的新词汇时扩展现有的文法词汇。