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Modeling word and morpheme order in natural language as an efficient trade-off of memory and surprisal.
Psychological Review ( IF 5.4 ) Pub Date : 2021-04-01 , DOI: 10.1037/rev0000269
Michael Hahn 1 , Judith Degen 1 , Richard Futrell 2
Affiliation  

Memory limitations are known to constrain language comprehension and production, and have been argued to account for crosslinguistic word order regularities. However, a systematic assessment of the role of memory limitations in language structure has proven elusive, in part because it is hard to extract precise large-scale quantitative generalizations about language from existing mechanistic models of memory use in sentence processing. We provide an architecture-independent information-theoretic formalization of memory limitations which enables a simple calculation of the memory efficiency of languages. Our notion of memory efficiency is based on the idea of a memory-surprisal trade-off: A certain level of average surprisal per word can only be achieved at the cost of storing some amount of information about the past context. Based on this notion of memory usage, we advance the Efficient Trade-off Hypothesis: The order of elements in natural language is under pressure to enable favorable memory-surprisal trade-offs. We derive that languages enable more efficient trade-offs when they exhibit information locality: When predictive information about an element is concentrated in its recent past. We provide empirical evidence from three test domains in support of the Efficient Trade-off Hypothesis: A reanalysis of a miniature artificial language learning experiment, a large-scale study of word order in corpora of 54 languages, and an analysis of morpheme order in two agglutinative languages. These results suggest that principles of order in natural language can be explained via highly generic cognitively motivated principles and lend support to efficiency-based models of the structure of human language. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

中文翻译:

将自然语言中的单词和语素顺序建模为记忆和惊奇的有效权衡。

众所周知,记忆限制会限制语言理解和产生,并被认为可以解释跨语言的词序规律。然而,对记忆限制在语言结构中的作用的系统评估已被证明是难以捉摸的,部分原因是很难从句子处理中记忆使用的现有机械模型中提取关于语言的精确大规模定量概括。我们提供了一种与体系结构无关的内存限制信息理论形式化,它可以简单地计算语言的内存效率。我们的记忆效率概念基于记忆-惊喜权衡的想法:每个单词的一定平均惊喜水平只能以存储一些关于过去上下文的信息为代价。基于这种内存使用的概念,我们提出了有效权衡假设:自然语言中元素的顺序处于压力之下,以实现有利的内存-意外权衡。我们推断,当语言表现出信息局部性时,语言可以实现更有效的权衡:当有关元素的预测信息集中在其最近的过去时。我们提供了来自三个测试领域的经验证据来支持有效权衡假设:对微型人工语言学习实验的重新分析、对 54 种语言语料库中词序的大规模研究以及对两种语素顺序的分析粘着语言。这些结果表明,自然语言中的顺序原则可以通过高度通用的认知动机原则来解释,并支持基于效率的人类语言结构模型。(PsycInfo 数据库记录 (c) 2021 APA,保留所有权利)。
更新日期:2021-04-01
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