通过词频共现规律，人和神经网络可学会主谓一致。
Collocational bootstrapping: A hypothesis about the learning of subject-verb agreement in humans and neural networks
- 利用词汇共现模式中的统计规律辅助语法学习。
- 在适度可预测的语料中，模型能稳定习得主谓一致规则。
- 该机制与儿童语言输入特点匹配，适合解释语言习得。
语言输入中的统计信号如何促进语法习得？本文提出一种称为共现自举（collocational bootstrapping）的机制，即词汇共现模式中的规律可为句法依赖提供线索。我们通过训练神经网络在不同可预测性水平的合成数据集上模拟语言习得，发现存在一个可预测性范围，在此范围内统计学习者能稳健习得英语主谓一致。随后我们分析了面向儿童的语言中主谓搭配的变异性，发现其变异性恰好落在支持稳健泛化的范围内。这些结果表明，共现自举是儿童所接收输入下可行的语言习得策略。
原文摘要 · Abstract (English)
In what ways might statistical signals in linguistic input assist with the acquisition of syntax? Here we hypothesize a mechanism called collocational bootstrapping, in which regularities in word co-occurrence patterns can provide cues to syntactic dependencies. We investigate whether this mechanism can support the acquisition of English subject-verb agreement. First, we simulate language acquisition by training neural networks on synthetic datasets that vary in how predictable their subject-verb pairings are. We find that there is a range of variability levels at which these statistical learners robustly learn subject-verb agreement. We then analyze the variability of subject-verb pairings in child-directed language, and we find that the variability in such data falls within the range that supported robust generalization in our computational simulations. Taken together, these results suggest that collocational bootstrapping is a viable learning strategy for the type of input that children receive.
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