Learning with hidden structure in Optimality Theory and Harmonic Grammar: beyond Robust Interpretive Parsing*
Learning with hidden structure in Optimality Theory and Harmonic Grammar: beyond Robust Interpretive Parsing*
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最优性理论和调和语法中的隐藏结构学习：超越鲁棒解释性解析*
DOI：
10.1017/s0952675713000031
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发表时间：
2013
期刊：
影响因子：
1.3
通讯作者：
G. Jarosz
中科院分区：
文献类型：
--
作者：
G. Jarosz
本文以音系学中一个突出的主要可学习性问题：面对隐藏结构的学习为背景，探讨了约束排序与加权的相对优点。具体来说，本文研究了一种众所周知的解决结构歧义问题的方法——稳健解释解析（RIP; Tesar & Smolensky, 1998），重点关注了Boersma（2003）首次描述的随机扩展。揭示了RIP随机公式的两个相关问题，其根源在于未能充分利用学习者语法中可用的概率信息。为了解决这些问题，引入了两种新的解析策略，并将其应用于概率排序和加权的学习算法。新的解析策略显著提高了学习者的学习成绩，不对称地提高了学习者的学习成绩。一旦RIP被提议的修改所取代，HG相对于以前工作中报告的OT学习者的明显优势就消失了（Boersma & Pater 2008）。
This paper explores the relative merits of constraint ranking vs. weighting in the context of a major outstanding learnability problem in phonology: learning in the face of hidden structure. Specifically, the paper examines a well-known approach to the structural ambiguity problem, Robust Interpretive Parsing (RIP; Tesar & Smolensky 1998), focusing on its stochastic extension first described by Boersma (2003). Two related problems with the stochastic formulation of RIP are revealed, rooted in a failure to take full advantage of probabilistic information available in the learner's grammar. To address these problems, two novel parsing strategies are introduced and applied to learning algorithms for both probabilistic ranking and weighting. The novel parsing strategies yield significant improvements in performance, asymmetrically improving performance of OT learners. Once RIP is replaced with the proposed modifications, the apparent advantage of HG over OT learners reported in previous work disappears (Boersma & Pater 2008).