Learning Additional Languages as Hierarchical Probabilistic Inference: Insights From First Language Processing.

Learning Additional Languages as Hierarchical Probabilistic Inference: Insights From First Language Processing.
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学习其他语言作为分层概率推理:来自第一语言处理的见解。

DOI:
10.1111/lang.12168
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发表时间:
2016
期刊:
影响因子:
4.4
通讯作者:
Jaeger,TFlorian
Jaeger,TFlorian
中科院分区:
人文科学1区
文献类型:
--
作者:
Pajak,Bozena;Fine,AlexB;Kleinschmidt,DaveF;Jaeger,TFlorian

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我们提出了一个第二语言和其他语言(L2/Ln)的习得框架,其动机是最近的社会索引知识在第一语言(L1)处理方面的工作。语言类别的分布与社会指数变量(例如,说话者身份、性别、方言)。我们总结了这种协方差的隐含概率知识对L1处理至关重要的证据,并提出L2/Lnlearning使用相同类型的社会索引信息来概率性地推断先前学习的和新语言的潜在层次结构。这种结构根据新语言在该等级中的推断位置指导新语言的习得,并且根据来自任何语言的新输入不断进行修改。这个建议将L1加工和L2/Ln习得统一为社会指数结构不确定性下的概率推理。它还提供了一个新的视角,在L2/Lnlearning跨语言的影响,容纳梯度和继续迁移(消极和积极的)从以前学到的新的语言,反之亦然。
We present a framework of second and additional language (L2/Ln) acquisition motivated by recent work on socio‐indexical knowledge in first language (L1) processing. The distribution of linguistic categories covaries with socio‐indexical variables (e.g., talker identity, gender, dialects). We summarize evidence that implicit probabilistic knowledge of this covariance is critical to L1 processing, and propose that L2/Lnlearning uses the same type of socio‐indexical information to probabilistically infer latent hierarchical structure over previously learned and new languages. This structure guides the acquisition of new languages based on their inferred place within that hierarchy and is itself continuously revised based on new input from any language. This proposal unifies L1 processing and L2/Lnacquisition as probabilistic inference under uncertainty over socio‐indexical structure. It also offers a new perspective on crosslinguistic influences during L2/Lnlearning, accommodating gradient and continued transfer (both negative and positive) from previously learned to novel languages, and vice versa.
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