Learning to capture text-table alignment is essential for tasks like
text-to-SQL. A model needs to correctly recognize natural language references
to columns and values and to ground them in the given database schema. In this
paper, we present a novel weakly supervised Structure-Grounded pretraining
framework (StruG) for text-to-SQL that can effectively learn to capture
text-table alignment based on a parallel text-table corpus. We identify a set
of novel prediction tasks: column grounding, value grounding and column-value
mapping, and leverage them to pretrain a text-table encoder. Additionally, to
evaluate different methods under more realistic text-table alignment settings,
we create a new evaluation set Spider-Realistic based on Spider dev set with
explicit mentions of column names removed, and adopt eight existing text-to-SQL
datasets for cross-database evaluation. STRUG brings significant improvement
over BERT-LARGE in all settings. Compared with existing pretraining methods
such as GRAPPA, STRUG achieves similar performance on Spider, and outperforms
all baselines on more realistic sets. The Spider-Realistic dataset is available
at this https URL

StruG 是一种基于弱监督预训练框架的文本转 SQL 方法，在列基础和值基础映射方面，使用 Spider-Realistic 数据集进行评估，并在多个数据集上进行跨库评估，证明其在所有设定上都显著优于 BERT-LARGE，并且在更现实的情况下优于其他先前的预训练方法。

文本到 SQL 的结构驱动预训练

Structure-Grounded Pretraining for Text-to-SQL

Face image quality is an important factor to enable high performance face
recognition systems. Face quality assessment aims at estimating the suitability
of a face image for recognition. Previous work proposed supervised solutions
that require artificially or human labelled quality values. However, both
labelling mechanisms are error-prone as they do not rely on a clear definition
of quality and may not know the best characteristics for the utilized face
recognition system. Avoiding the use of inaccurate quality labels, we proposed
a novel concept to measure face quality based on an arbitrary face recognition
model. By determining the embedding variations generated from random
subnetworks of a face model, the robustness of a sample representation and
thus, its quality is estimated. The experiments are conducted in a
cross-database evaluation setting on three publicly available databases. We
compare our proposed solution on two face embeddings against six
state-of-the-art approaches from academia and industry. The results show that
our unsupervised solution outperforms all other approaches in the majority of
the investigated scenarios. In contrast to previous works, the proposed
solution shows a stable performance over all scenarios. Utilizing the deployed
face recognition model for our face quality assessment methodology avoids the
training phase completely and further outperforms all baseline approaches by a
large margin. Our solution can be easily integrated into current face
recognition systems and can be modified to other tasks beyond face recognition.

本文提出了一种基于随机子网络的人脸图像质量评估方法，使用嵌入变化来估计样本表示的健壮性和相应的图像质量，并在三个公开数据库上进行了交叉数据库评估。实验表明，此方法超过了其他学术和工业界的六种方法，表现出稳定的性能。