CARTE: Pretraining and Transfer for Tabular Learning

• URL: http://arxiv.org/abs/2402.16785v2
• Date: Fri, 31 May 2024 15:03:11 GMT
• Title: CARTE: Pretraining and Transfer for Tabular Learning
• Authors: Myung Jun Kim, Léo Grinsztajn, Gaël Varoquaux,
• Abstract summary: We propose a neural architecture that does not need such correspondences. As a result, we can pretrain it on background data that has not been matched. A benchmark shows that CARTE facilitates learning, outperforming a solid set of baselines.
• Score: 10.155109224816334
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Pretrained deep-learning models are the go-to solution for images or text. However, for tabular data the standard is still to train tree-based models. Indeed, transfer learning on tables hits the challenge of data integration: finding correspondences, correspondences in the entries (entity matching) where different words may denote the same entity, correspondences across columns (schema matching), which may come in different orders, names... We propose a neural architecture that does not need such correspondences. As a result, we can pretrain it on background data that has not been matched. The architecture -- CARTE for Context Aware Representation of Table Entries -- uses a graph representation of tabular (or relational) data to process tables with different columns, string embedding of entries and columns names to model an open vocabulary, and a graph-attentional network to contextualize entries with column names and neighboring entries. An extensive benchmark shows that CARTE facilitates learning, outperforming a solid set of baselines including the best tree-based models. CARTE also enables joint learning across tables with unmatched columns, enhancing a small table with bigger ones. CARTE opens the door to large pretrained models for tabular data.

Related papers

• Relational Deep Learning: Graph Representation Learning on Relational Databases [69.7008152388055]
  We introduce an end-to-end representation approach to learn on data laid out across multiple tables. Message Passing Graph Neural Networks can then automatically learn across the graph to extract representations that leverage all data input.
  arXiv  Detail & Related papers  (2023-12-07T18:51:41Z)
• Table Retrieval May Not Necessitate Table-specific Model Design [83.27735758203089]
  We focus on the task of table retrieval, and ask: "is table-specific model design necessary for table retrieval?" Based on an analysis on a table-based portion of the Natural Questions dataset (NQ-table), we find that structure plays a negligible role in more than 70% of the cases. We then experiment with three modules to explicitly encode table structures, namely auxiliary row/column embeddings, hard attention masks, and soft relation-based attention biases. None of these yielded significant improvements, suggesting that table-specific model design may not be necessary for table retrieval.
  arXiv  Detail & Related papers  (2022-05-19T20:35:23Z)
• TGRNet: A Table Graph Reconstruction Network for Table Structure Recognition [76.06530816349763]
  We propose an end-to-end trainable table graph reconstruction network (TGRNet) for table structure recognition. Specifically, the proposed method has two main branches, a cell detection branch and a cell logical location branch, to jointly predict the spatial location and the logical location of different cells.
  arXiv  Detail & Related papers  (2021-06-20T01:57:05Z)
• GitTables: A Large-Scale Corpus of Relational Tables [3.1218214157681277]
  We introduce GitTables, a corpus of 1M relational tables extracted from GitHub. Analyses of GitTables show that its structure, content, and topical coverage differ significantly from existing table corpora. We present three applications of GitTables, demonstrating its value for learned semantic type detection models, completion methods, and benchmarks for table-to-KG matching, data search, and preparation.
  arXiv  Detail & Related papers  (2021-06-14T09:22:09Z)
• TABBIE: Pretrained Representations of Tabular Data [22.444607481407633]
  We devise a simple pretraining objective that learns exclusively from tabular data. Unlike competing approaches, our model (TABBIE) provides embeddings of all table substructures. A qualitative analysis of our model's learned cell, column, and row representations shows that it understands complex table semantics and numerical trends.
  arXiv  Detail & Related papers  (2021-05-06T11:15:16Z)
• Retrieving Complex Tables with Multi-Granular Graph Representation Learning [20.72341939868327]
  The task of natural language table retrieval seeks to retrieve semantically relevant tables based on natural language queries. Existing learning systems treat tables as plain text based on the assumption that tables are structured as dataframes. We propose Graph-based Table Retrieval (GTR), a generalizable NLTR framework with multi-granular graph representation learning.
  arXiv  Detail & Related papers  (2021-05-04T20:19:03Z)
• TCN: Table Convolutional Network for Web Table Interpretation [52.32515851633981]
  We propose a novel table representation learning approach considering both the intra- and inter-table contextual information. Our method can outperform competitive baselines by +4.8% of F1 for column type prediction and by +4.1% of F1 for column pairwise relation prediction.
  arXiv  Detail & Related papers  (2021-02-17T02:18:10Z)
• A Graph Representation of Semi-structured Data for Web Question Answering [96.46484690047491]
  We propose a novel graph representation of Web tables and lists based on a systematic categorization of the components in semi-structured data as well as their relations. Our method improves F1 score by 3.90 points over the state-of-the-art baselines.
  arXiv  Detail & Related papers  (2020-10-14T04:01:54Z)
• GraPPa: Grammar-Augmented Pre-Training for Table Semantic Parsing [117.98107557103877]
  We present GraPPa, an effective pre-training approach for table semantic parsing. We construct synthetic question-pairs over high-free tables via a synchronous context-free grammar. To maintain the model's ability to represent real-world data, we also include masked language modeling.
  arXiv  Detail & Related papers  (2020-09-29T08:17:58Z)
• Identifying Table Structure in Documents using Conditional Generative Adversarial Networks [0.0]
  In many industries and in academic research, information is primarily transmitted in the form of unstructured documents. We propose a top-down approach, first using a conditional generative adversarial network to map a table image into a standardised skeleton' table form. We then deriving latent table structure using xy-cut projection and Genetic Algorithm optimisation.
  arXiv  Detail & Related papers  (2020-01-13T20:42:40Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.