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 subject and "bone" as object 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 corpus 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 subject 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 subjecthood and objecthood in a minority of naturally occurring transitive clauses; 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.").

翻译：语法线索在自然语言中有时与词汇含义冗余。例如，英语词序规则约束了像"The dog chewed the bone"这类句子的语序，尽管"dog"作为主语、"bone"作为宾语的身份可从世界知识和合理性推断。量化此类冗余的发生频率及其在类型学多样语言中的差异，有助于阐明语法的功能与演化。为此，我们开展了英语和俄语的行为实验，以及跨语言计算分析，以测量从语料库文本中提取的及物小句中语法线索的冗余程度。实验中，英语和俄语受试者（n=484）被呈现从自然语句中提取的主语、动词和宾语（随机排列，去除形态标记），需识别执行动作的名词。两种语言的正确率均较高（英语约89%，俄语约87%）。随后，我们训练了一个神经网络分类器完成类似任务：预测主-谓-宾三元组中哪个名词是主语。跨越8个语系的30种语言，分类性能始终稳定：中位正确率达87%，与人类实验观测水平相当。结论是：在少数自然发生的及物小句中，词序等语法线索对表达主语与宾语身份是必要的；然而，它们既可（a）提供重要的冗余信息，又（b）对传达无法仅从词汇推断的意图至关重要——包括描述角色常可互换的人际互动（如Ray helped Lu / Lu helped Ray），以及表达非典型意义（如"The bone chewed the dog"）。