Abstract
Table-to-Text has been traditionally approached as a linear language to text problem. However, visually represented tables are rich in visual information and serve as a concise, effective form of representing data and its relationships. When using text-based approaches, after the linearization process, this information is either lost or represented in a space inefficient manner. This inefficiency has remained a constant challenge for text-based approaches making them struggle with large tables. In this paper, we demonstrate that image representation of tables are more space-efficient than the typical textual linearizations, and multi-modal approaches are competitive in Table-to-Text tasks. We present PixT3, a multimodal table-to-text model that outperforms the state-of-the-art (SotA) in the ToTTo benchmark in a pure Table-to-Text setting while remaining competitive in controlled Table-to-Text scenarios. It also generalizes better in unseen datasets, outperforming ToTTo SotA in all generation settings. Additionally, we introduce a new intermediate training curriculum to reinforce table structural awareness, leading to improved generation and overall faithfulness of the models.
Abstract (translated)
表格到文本（Table-to-Text）问题一直以来都被视为一个线性语言到文本问题。然而，视觉表示的表格富含视觉信息，可以作为表示数据及其关系的简洁而有效的形式。当使用基于文本的方法时，在线性化过程之后，这些信息要么丢失，要么以空间效率低的方式表示。这一低效率一直成为基于文本方法的挑战，使它们在处理大量表格时遇到困难。在本文中，我们证明了表格图像表示比典型的文本线性化更具有空间效率，多模态方法在表格到文本任务中具有竞争力。我们提出了PixT3，一种多模态表格到文本模型，在纯表格到文本设置中超过了最先进的（SotA）水平，同时在受控的表格到文本场景中也具有竞争力。此外，它在对未知数据集的泛化方面也表现更好，在所有生成设置中超过了TotTo SotA。最后，我们还引入了一种新的中间训练课程，以增强表格结构的意识，从而提高了模型的生成和整体可靠性。
URL
https://arxiv.org/abs/2311.09808
