研究发现，儿童语言更利于学词，但关键在口语性而非专为儿童设计。
Is Child-Directed Language Optimized for Word Learning? A Computational Study of Verb Meaning Acquisition
- 用神经模型对比儿童与成人语言，剥离语法或词汇关联
- 口语化语言训练的模型在学动词时表现更好，尤其儿童语言
- 动词意义先于语法掌握，口语优势可能源于语言形式本身
本研究通过在儿童语言（CDL）和成人语言（ADL）数据上训练神经语言模型，探究其是否优化了语言学习。通过有选择地移除语法或词汇共现信息，评估对动词意义习得的影响。结果显示，破坏语法会损害所有数据集的学习效果，但在口语语料（尤其是儿童语言）上训练的模型表现出显著更强的鲁棒性。跟踪训练过程中的语义与语法表现发现，存在语义优先的发展轨迹——动词意义在语法能力稳固前就已形成，这种异步性在口语领域尤为明显，尤其在儿童语言中。结果表明，以往归因于儿童语言的优势，可能更多源于口语表达的整体特性，而非其专为儿童设计的特异性优化。
原文摘要 · Abstract (English)
Is child-directed language (CDL) optimized to support language learning, and which aspects of linguistic development does it facilitate? We investigate this question using neural language models trained on CDL versus adult-directed language (ADL). We selectively remove syntactic or lexical co-occurrence information from the model training data, and evaluate the impact of these manipulations on verb meaning acquisition. While disrupting syntax impairs learning across all datasets, models trained on CDL and spoken ADL show significantly higher resilience than those trained on written input. Tracking semantic and syntactic performance over training, we observe a semantic-first trajectory, with verb meanings emerging prior to robust syntactic proficiency, an asynchrony most pronounced in the spoken domain, especially CDL. These results suggest that the advantage for verb learning previously attributed to CDL may instead reflect broader properties of the spoken register, rather than a uniquely CDL-specific optimization.
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