Model inference, such as model comparison, model checking, and model
selection, is an important part of model development. Leave-one-out
cross-validation (LOO) is a general approach for assessing the generalizability
of a model, but unfortunately, LOO does not scale well to large datasets. We
propose a combination of using approximate inference techniques and
probability-proportional-to-size-sampling (PPS) for fast LOO model evaluation
for large datasets. We provide both theoretical and empirical results showing
good properties for large data.

模型推理是模型开发的重要部分，Leave-one-out 交叉验证方法在评估模型泛化能力方面普遍适用，但是不适用于大型数据集。我们提出了一种结合近似推理技术和大小为概率比例采样的方法，用于快速评估大型数据集的 LOO 模型，提供了理论和实证结果来说明其性能优良。

大数据贝叶斯留一验证

Bayesian leave-one-out cross-validation for large data

Distinguishing between arguments and adjuncts of a verb is a longstanding,
nontrivial problem. In natural language processing, argumenthood information is
important in tasks such as semantic role labeling (SRL) and prepositional
phrase (PP) attachment disambiguation. In theoretical linguistics, many
diagnostic tests for argumenthood exist but they often yield conflicting and
potentially gradient results. This is especially the case for syntactically
oblique items such as PPs. We propose two PP argumenthood prediction tasks
branching from these two motivations: (1) binary argument-adjunct
classification of PPs in VerbNet, and (2) gradient argumenthood prediction
using human judgments as gold standard, and report results from prediction
models that use pretrained word embeddings and other linguistically informed
features. Our best results on each task are (1) $acc.=0.955$, $F_1=0.954$
(ELMo+BiLSTM) and (2) Pearson's $r=0.624$ (word2vec+MLP). Furthermore, we
demonstrate the utility of argumenthood prediction in improving sentence
representations via performance gains on SRL when a sentence encoder is
pretrained with our tasks.

提出两种 PP 类别预测任务 (二分和梯度预测)，并使用预先训练的词向量和其他基于语言学的特征来预测 PP 的类别，这有助于改进语句表示并提高 SRL 的性能。