Distilling large, unstructured text into a structured, condensed form such as
tables is an open research problem. One of the primary challenges in
automatically generating tables is ensuring their syntactic validity. Prior
approaches address this challenge by including additional parameters in the
Transformer's attention mechanism to attend to specific rows and column
headers. In contrast to this single-stage method, this paper presents a
two-stage approach called Generative Tables (gTBLS). The first stage infers
table structure (row and column headers) from the text. The second stage
formulates questions using these headers and fine-tunes a causal language model
to answer them. Furthermore, the gTBLS approach is amenable to the utilization
of pre-trained Large Language Models in a zero-shot configuration, presenting a
solution for table generation in situations where fine-tuning is not feasible.
gTBLS improves prior approaches by up to 10% in BERTScore on the table
construction task and up to 20% on the table content generation task of the
E2E, WikiTableText, WikiBio, and RotoWire datasets.

通过生成表格的两个阶段方法（Generative Tables，gTBLS），从结构化文本中提取表格结构和表格内容，并利用预训练大型语言模型的零样本配置，改进先前方法并在多个数据集上提高 BERTScore 达 10% 至 20%。

通过条件问答生成表格的 gTBLS

gTBLS: Generating Tables from Text by Conditional Question Answering

This paper presents a high-quality multilingual dataset for the documentation
domain to advance research on localization of structured text. Unlike
widely-used datasets for translation of plain text, we collect XML-structured
parallel text segments from the online documentation for an enterprise software
platform. These Web pages have been professionally translated from English into
16 languages and maintained by domain experts, and around 100,000 text segments
are available for each language pair. We build and evaluate translation models
for seven target languages from English, with several different copy mechanisms
and an XML-constrained beam search. We also experiment with a non-English pair
to show that our dataset has the potential to explicitly enable $17 \times 16$
translation settings. Our experiments show that learning to translate with the
XML tags improves translation accuracy, and the beam search accurately
generates XML structures. We also discuss trade-offs of using the copy
mechanisms by focusing on translation of numerical words and named entities. We
further provide a detailed human analysis of gaps between the model output and
human translations for real-world applications, including suitability for
post-editing.

本文针对结构化文本本地化的研究，提出了一种高质量的多语种数据集，并构建和评估了针对七种目标语言的翻译模型，实验结果表明，使用 XML 标签可以提高翻译精确度。