Even in this era of parameter-heavy statistical modeling requiring large training datasets, we believe explicit symbolic models of grammar have much to offer, especially when it comes to modeling complex syntactic phenomena using a minimal number of parameters. It is the goal of explanatory symbolic models to make explicit a minimal set of features that license phrase structure, and thus, they should be of interest to engineers seeking parameter-efficient language models. Relative clauses have been much studied and have a long history in linguistics. We contribute a feature-driven account of the formation of a variety of basic English relative clauses in the Minimalist Program framework that is precisely defined, descriptively adequate, and computationally feasible in the sense that we have not observed an exponential scaling with the number of heads in the Lexical Array. Following previous work, we assume an analysis involving a uT feature and uRel feature, possibly simultaneously valued. In this article, we show a detailed mechanical implementation of this analysis and describe the structures computed for that, which, and who/whom relatives for standard English.

中文翻译：

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英语关系从句的计算模型研究

即使在这个需要大量训练数据集的参数密集型统计建模时代，我们也相信显式语法符号模型可以提供很多帮助，特别是在使用最少数量的参数对复杂句法现象进行建模时。解释性符号模型的目标是明确许可短语结构的最小特征集，因此，寻求参数高效语言模型的工程师应该对它们感兴趣。关系从句在语言学中得到了广泛的研究并且有着悠久的历史。我们对极简程序框架中各种基本英语关系从句的形成做出了特征驱动的解释，该框架定义精确，描述充分，并且在计算上可行，因为我们没有观察到头部数量的指数缩放在词汇数组中。根据之前的工作，我们假设分析涉及 uT 特征和 uRel 特征，可能同时赋值。在本文中，我们展示了该分析的详细机械实现，并描述了计算的结构那,哪个， 和谁/谁标准英语的亲戚。