论文标题
通过分裂和共同神经网络学习语法
Learning grammar with a divide-and-concur neural network
论文作者
论文摘要
我们对无上下文的语法推断实施了分裂和分配的迭代投影方法。与大多数最新的自然语言处理模型不同，我们的方法需要相对较少的离散参数，从而使推断的语法可以直接解释 - 可以从解决方案中读取如何构建语法有效的句子。我们方法的另一个优点是，与许多其他模型所采用的数百GB培训数据相比，仅几句句子从几句句子中推断出有意义的语法规则。我们演示了几种应用方法的方法：分类单词并从头开始推断语法，采用现有语法并完善其类别和规则，并采用现有的语法并扩大其词典，因为它在新数据中遇到新单词时。
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.
