Computational Studies of Parameter Setting in Language

发展适当且可行的语言参数设置计算理论的问题是语言理论、语言习得、学习理论和认知科学的核心问题。该补助金的目的是作出显着增加，与以前的研究相比，在语法参数空间的大小和范围，并做计算参数设置这些参数空间的调查。我们计划研究基于8个参数（加上一个“词汇”参数）的256个语法或更大的空间的学习。 我们将研究的主要问题包括：1。对于更大的参数集，参数设置理论是否有效（即，对于所有的自然语言来说，这些参数，算法是否收敛）？ 2.不同的算法比其他算法更好吗？ 3.在我们研究的特定参数空间中，关于标记性或默认值的特殊假设是什么？是否有标记性或默认值的一般原则似乎适用于许多不同的参数？默认值通常是必需的吗？ 4.当我们“按比例放大”时，参数设置中的计算结果会发生什么？也就是说，如果一个参数空间得到了某种结果模式，那么当这个空间嵌入到一个更大的空间中时，我们会发现这种模式仍然存在吗？或者是小空间的产物？ 5.假设算法收敛于我们已知被实例化的一类参数设置的正确参数设置（即，存在展现这些参数设置的自然语言），但是对于某些其它参数设置不收敛。我们能否证明这些参数设置不是用自然语言实例化的，因此实际上存在一些参数设置不显示的可学习性原因？ 6.特定算法收敛的速度有多快？这可以用需要给出的例子的数量来说明。在这方面，有些算法比其他算法更现实吗？ 7.算法的性质与语言习得的实证研究有什么关系？早期的采集特性是否与算法有关？

The problem of developing an appropriate and workable computational theory of parameter-setting in language is a central problem for linguistic theory, language acquisition, learning theory and cognitive science in general. The purpose of this grant is to make a significant increase, compared to previous studies, in the size and scope of the syntactic parameter spaces, and to do computational parameter-setting investigations of these parameter spaces. We plan to investigate the learning of a space of 256 grammars or larger, based on 8 parameters (plus a "lexical" parameter). The major questions that we will investigate include: 1. With a larger set of parameters, does the theory of parameter-setting work (i.e., for all natural languages stated in terms of those parameters, does the algorithm converge)? 2. Do different algorithms work better than others? 3. What are the kinds of special assumptions about markedness or default values that work in the specific parameter spaces that we study? Are there general principles of markedness or default values that seem to apply to many different parameters? Are default values necessary in general? 4. What happens to computational results in parameter-setting when we "scale up" ? That is, if a certain pattern of results obtains for a parameter-space, do we find that this pattern remains when the space is embedded in a larger space? Or were the results artifacts of the smaller space? 5. Suppose that an algorithm converges on the correct parameter-settings for a class of parameter-settings that we know is instantiated (i.e., there are natural languages which exhibit these parameter-settings), but doesn't converge for some other parameter-settings. Can we show that these parameter-settings are not instantiated in natural language, so that in fact there are reasons of learnability that some parameter-settings don't show up? 6. How fast do particular algorithms converge? This can be stated in terms of the number of examples t hat need to be given. Are some algorithms more realistic than others in this regard? 7. What is the relation of the properties of the algorithms to empirical research in language acquisition? Can early properties of acquisition be shown to relate to the algorithms?