Paper Reading AI Learner

PixT3: Pixel-based Table To Text generation

2023-11-16 11:32:47
Iñigo Alonso, Eneko Agirre, Mirella Lapata


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。



3D Action Action_Localization Action_Recognition Activity Adversarial Agent Attention Autonomous Bert Boundary_Detection Caption Chat Classification CNN Compressive_Sensing Contour Contrastive_Learning Deep_Learning Denoising Detection Dialog Diffusion Drone Dynamic_Memory_Network Edge_Detection Embedding Embodied Emotion Enhancement Face Face_Detection Face_Recognition Facial_Landmark Few-Shot Gait_Recognition GAN Gaze_Estimation Gesture Gradient_Descent Handwriting Human_Parsing Image_Caption Image_Classification Image_Compression Image_Enhancement Image_Generation Image_Matting Image_Retrieval Inference Inpainting Intelligent_Chip Knowledge Knowledge_Graph Language_Model Matching Medical Memory_Networks Multi_Modal Multi_Task NAS NMT Object_Detection Object_Tracking OCR Ontology Optical_Character Optical_Flow Optimization Person_Re-identification Point_Cloud Portrait_Generation Pose Pose_Estimation Prediction QA Quantitative Quantitative_Finance Quantization Re-identification Recognition Recommendation Reconstruction Regularization Reinforcement_Learning Relation Relation_Extraction Represenation Represenation_Learning Restoration Review RNN Salient Scene_Classification Scene_Generation Scene_Parsing Scene_Text Segmentation Self-Supervised Semantic_Instance_Segmentation Semantic_Segmentation Semi_Global Semi_Supervised Sence_graph Sentiment Sentiment_Classification Sketch SLAM Sparse Speech Speech_Recognition Style_Transfer Summarization Super_Resolution Surveillance Survey Text_Classification Text_Generation Tracking Transfer_Learning Transformer Unsupervised Video_Caption Video_Classification Video_Indexing Video_Prediction Video_Retrieval Visual_Relation VQA Weakly_Supervised Zero-Shot