Abstract The present paper presents a multimodel inference approach to linguistic variation, expanding on prior work by Kuperman and Bresnan (2012). We argue that corpus data often present the analyst with high model selection uncertainty. This uncertainty is inevitable given that language is highly redundant: every feature is predictable from multiple other features. However, uncertainty involved in model selection is ignored by the standard method of selecting the single best model and inferring the effects of the predictors under the assumption that the best model is true. Multimodel inference avoids committing to a single model. Rather, we make predictions based on the entire set of plausible models, with contributions of models weighted by the models' predictive value. We argue that multimodel inference is superior to model selection for both the I-Language goal of inferring the mental grammars that generated the corpus, and the E-Language goal of predicting characteristics of future speech samples from the community represented by the corpus. Applying multimodel inference to the classic problem of English auxiliary contraction, we show that the choice between multimodel inference and model selection matters in practice: the best model may contain predictors that are not significant when the full set of plausible models is considered, and may omit predictors that are significant considering the full set of models. We also contribute to the study of English auxiliary contraction. We document the effects of priming, contextual predictability, and specific syntactic constructions and provide evidence against effects of phonological context.

中文翻译：

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一种用于分类变体选择的多模型推理方法：构造，启动和频率对am，are和is的完全形式和收缩形式之间选择的影响

摘要本文提出了一种用于语言变异的多模型推理方法，在Kuperman和Bresnan（2012）的先前工作的基础上进行了扩展。我们认为，语料库数据通常会给分析人员带来较高的模型选择不确定性。鉴于语言是高度冗余的，因此这种不确定性是不可避免的：每个功能都可以从多个其他功能中预测出来。但是，选择最佳模型并在假设最佳模型为真的情况下推断预测变量的影响的标准方法会忽略模型选择中涉及的不确定性。多模型推断可避免提交到单个模型。相反，我们基于整个合理的模型集进行预测，并通过模型的预测值对模型的贡献进行加权。我们认为，对于推断生成语料库的心理语法的I语言目标和从语料库所代表的社区预测未来语音样本特征的E语言目标，多模型推理均优于模型选择。将多模型推论应用于英语辅助收缩的经典问题，我们证明了在实践中多模型推论和模型选择之间的选择很重要：当考虑所有可能的模型时，最佳模型可能包含不重要的预测变量，并且可能会省略考虑到全套模型的预测因素很重要。我们也为英语辅助收缩的研究做出贡献。我们记录了启动，上下文可预测性，