Formatting is an important property in tables for visualization,
presentation, and analysis. Spreadsheet software allows users to automatically
format their tables by writing data-dependent conditional formatting (CF)
rules. Writing such rules is often challenging for users as it requires them to
understand and implement the underlying logic. We present FormaT5, a
transformer-based model that can generate a CF rule given the target table and
a natural language description of the desired formatting logic. We find that
user descriptions for these tasks are often under-specified or ambiguous,
making it harder for code generation systems to accurately learn the desired
rule in a single step. To tackle this problem of under-specification and
minimise argument errors, FormaT5 learns to predict placeholders though an
abstention objective. These placeholders can then be filled by a second model
or, when examples of rows that should be formatted are available, by a
programming-by-example system. To evaluate FormaT5 on diverse and real
scenarios, we create an extensive benchmark of 1053 CF tasks, containing
real-world descriptions collected from four different sources. We release our
benchmarks to encourage research in this area. Abstention and filling allow
FormaT5 to outperform 8 different neural approaches on our benchmarks, both
with and without examples. Our results illustrate the value of building
domain-specific learning systems.

FormaT5 是一种基于 Transformer 的模型，能够根据目标表格和自然语言描述生成数据相关的条件格式规则。通过预测占位符并进行填充，FormaT5 在优化条件格式规则的指导下，在各种真实场景中优于其他 8 种神经方法。

FormaT5：条件表格式化的禁止和示例与自然语言

FormaT5: Abstention and Examples for Conditional Table Formatting with  Natural Language

Data management and analysis tasks are often carried out using spreadsheet
software. A popular feature in most spreadsheet platforms is the ability to
define data-dependent formatting rules. These rules can express actions such as
"color red all entries in a column that are negative" or "bold all rows not
containing error or failure." Unfortunately, users who want to exercise this
functionality need to manually write these conditional formatting (CF) rules.
We introduce CORNET, a system that automatically learns such conditional
formatting rules from user examples. CORNET takes inspiration from inductive
program synthesis and combines symbolic rule enumeration, based on
semi-supervised clustering and iterative decision tree learning, with a neural
ranker to produce accurate conditional formatting rules. In this demonstration,
we show CORNET in action as a simple add-in to Microsoft Excel. After the user
provides one or two formatted cells as examples, CORNET generates formatting
rule suggestions for the user to apply to the spreadsheet.

CORNET 是一个利用数据样本自动学习条件格式化规则的系统，通过运用归纳程序合成、基于半监督聚类和迭代决策树学习的符号规则枚举以及神经排序器等方法，能够自动生成准确的条件格式化规则，它可以作为微软 Excel 的简单插件，通过用户提供的示例单元格，为用户生成适用于电子表格的格式化规则建议。