Language Learning as Language Use: A Cross-Linguistic Model of Child Language Development
Language Learning as Language Use: A Cross-Linguistic Model of Child Language Development
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DOI：
10.1037/rev0000126
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发表时间：
2019-01-01
影响因子：
5.4
通讯作者：
Christiansen, Morten H.
中科院分区：
文献类型：
--
作者：
McCauley, Stewart M.;Christiansen, Morten H.
虽然基于使用的语言发展方法得到了计算研究的大量支持，但很少有人尝试回答基于使用的理论所带来的关键计算挑战：成功地将语言学习建模为语言使用。我们提出了一个基于使用的语言习得的计算模型，学习在一个纯粹的增量方式，通过基于组块的在线处理。它提供了广泛的跨语言覆盖面，同时将理解和生产的关键方面统一在一个框架内。该模型的设计反映了语言处理的实时性所施加的记忆约束，并受到心理语言学证据的启发，儿童对多词序列的分布特性的敏感性和基于本地信息的浅层语言理解。它从儿童导向言语的语料库中学习。将输入的单词分块在一起以递增地构建基于项目的“浅解析”。“当模型遇到目标儿童发出的话语时，它会尝试使用理解过程中涉及的相同块和统计数据生成相同的话语。高性能的理解和生产相关的任务：该模型的浅层解析评估跨越79个独生子女语料库英语。法语和德语，而其生产性能是在超过200个独生子女语料库代表29种语言从CHIMES数据库进行评估。该模型还成功地捕捉到儿童的复杂句子类型的生产的结果。总之，我们的建模结果表明，许多儿童的早期语言行为可能是支持基于项目的学习，通过在线处理简单的分布线索，符合的概念，即收购可以理解为学习处理语言。
While usage-based approaches to language development enjoy considerable support from computational studies, there have been few attempts to answer a key computational challenge posed by usage-based theory: the successful modeling of language learning as language use. We present a usage-based computational model of language acquisition which learns in a purely incremental fashion, through online processing based on chunking. and which offers broad, cross-linguistic coverage while uniting key aspects of comprehension and production within a single framework. The model's design reflects memory constraints imposed by the real-time nature of language processing, and is inspired by psycholinguistic evidence for children's sensitivity to the distributional properties of multiword sequences and for shallow language comprehension based on local information. It learns from corpora of child-directed speech. chunking incoming words together to incrementally build an item-based "shallow parse." When the model encounters an utterance made by the target child, it attempts to generate an identical utterance using the same chunks and statistics involved during comprehension. High performance is achieved on both comprehension- and production-related tasks: the model's shallow parsing is evaluated across 79 single-child corpora spanning English. French, and German, while its production performance is evaluated across over 200 single-child corpora representing 29 languages from the CHIMES database. The model also succeeds in capturing findings from children's production of complex sentence types. Together, our modeling results suggest that much of children's early linguistic behavior may be supported by item-based learning through online processing of simple distributional cues, consistent with the notion that acquisition can be understood as learning to process language.