Grammatical cues are sometimes redundant with word meanings in natural
language. For instance, English word order rules constrain the word order of a
sentence like "The dog chewed the bone" even though the status of "dog" as
agent and "bone" as patient can be inferred from world knowledge and
plausibility. Quantifying how often this redundancy occurs, and how the level
of redundancy varies across typologically diverse languages, can shed light on
the function and evolution of grammar. To that end, we performed a behavioral
experiment in English and Russian and a cross-linguistic computational analysis
measuring the redundancy of grammatical cues in transitive clauses extracted
from naturalistic text. English and Russian speakers (n=484) were presented
with subjects, verbs, and objects (in random order and with morphological
markings removed) extracted from naturally occurring sentences and were asked
to identify which noun is the agent of the action. Accuracy was high in both
languages (~89% in English, ~87% in Russian). Next, we trained a neural network
machine classifier on a similar task: predicting which nominal in a
subject-verb-object triad is the subject. Across 30 languages from eight
language families, performance was consistently high: a median accuracy of 87%,
comparable to the accuracy observed in the human experiments. The conclusion is
that grammatical cues such as word order are necessary to convey agenthood and
patienthood in only at most 10-15% of naturally occurring sentences;
nevertheless, they can (a) provide an important source of redundancy and (b)
are crucial for conveying intended meaning that cannot be inferred from the
words alone, including descriptions of human interactions, where roles are
often reversible (e.g., Ray helped Lu/Lu helped Ray), and expressing
non-prototypical meanings (e.g., "The bone chewed the dog.").

通过行为实验和跨语言计算分析，我们研究了自然语言中语法提示的冗余性，并发现仅有最多 10-15％的自然发生的句子需要语法提示来表达代理性和病因性，但它们在提供重要的冗余来源方面至关重要，特别是在描述人际交往、表达非典型意义以及区分可逆角色时。此外，我们使用神经网络模型对名词在主谓宾三元组中的不同作用进行了预测，并在 30 种不同的语言中获得了一致高的准确率。