A Cross-Linguistic Pressure for Uniform Information Density in Word Order

IF 4.2 1区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
T. Clark, Clara Meister, Tiago Pimentel, Michael Hahn, Ryan Cotterell, Richard Futrell, Roger Levy Mit, E. Zurich, U. Cambridge, Saarland University, UC Irvine
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引用次数: 0

Abstract

Abstract While natural languages differ widely in both canonical word order and word order flexibility, their word orders still follow shared cross-linguistic statistical patterns, often attributed to functional pressures. In the effort to identify these pressures, prior work has compared real and counterfactual word orders. Yet one functional pressure has been overlooked in such investigations: The uniform information density (UID) hypothesis, which holds that information should be spread evenly throughout an utterance. Here, we ask whether a pressure for UID may have influenced word order patterns cross-linguistically. To this end, we use computational models to test whether real orders lead to greater information uniformity than counterfactual orders. In our empirical study of 10 typologically diverse languages, we find that: (i) among SVO languages, real word orders consistently have greater uniformity than reverse word orders, and (ii) only linguistically implausible counterfactual orders consistently exceed the uniformity of real orders. These findings are compatible with a pressure for information uniformity in the development and usage of natural languages.1
统一语序信息密度的跨语言压力
摘要尽管自然语言在规范语序和语序灵活性方面存在很大差异,但它们的语序仍然遵循共同的跨语言统计模式,这通常归因于功能压力。为了识别这些压力,先前的工作对真实语序和反事实语序进行了比较。然而,在这些研究中,有一种功能压力被忽视了:统一信息密度假说,认为信息应该在整个话语中均匀分布。在这里,我们要问UID的压力是否影响了语序模式的跨语言性。为此,我们使用计算模型来测试真实订单是否比反事实订单带来更大的信息一致性。在我们对10种类型多样的语言的实证研究中,我们发现:(i)在SVO语言中,真实语序始终比反向语序具有更大的一致性,以及(ii)只有在语言上不可信的反事实语序始终超过真实语序的一致性。这些发现与自然语言发展和使用中信息统一的压力相一致。1
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来源期刊
CiteScore
32.60
自引率
4.60%
发文量
58
审稿时长
8 weeks
期刊介绍: The highly regarded quarterly journal Computational Linguistics has a companion journal called Transactions of the Association for Computational Linguistics. This open access journal publishes articles in all areas of natural language processing and is an important resource for academic and industry computational linguists, natural language processing experts, artificial intelligence and machine learning investigators, cognitive scientists, speech specialists, as well as linguists and philosophers. The journal disseminates work of vital relevance to these professionals on an annual basis.
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