Grammatical inference is a classical problem in computational learning theory and a topic of wider influence in natural language processing. We treat grammars as a model of computation and propose a novel neural approach to induction of regular grammars from positive and negative examples. Our model is fully explainable, its intermediate results are directly interpretable as partial parses, and it can be used to learn arbitrary regular grammars when provided with sufficient data. Our method consistently attains high recall and precision scores across a range of tests of varying complexity. We make the detailed results and code readily available.

翻译：语法推论是计算学理论的一个古典问题,也是自然语言处理具有更广泛影响的话题。我们把语法作为计算模型处理,从正反例子中提出引入常规语法的新神经学方法。我们的模型完全可以解释,其中间结果可以直接解释为局部剖析,在提供足够数据时,可以用来学习任意的常规语法。我们的方法在一系列复杂程度不同的测试中始终能取得高记分和精确分数。我们很容易获得详细的结果和代码。