{"total":15,"rows":[{"id":"2106.12944","title":"AIT-QA: Question Answering Dataset over Complex Tables in the Airline Industry","created":"2021-06-24","cited_by_count":44,"area":null},{"id":"2201.09745","title":"Table Pre-training: A Survey on Model Architectures, Pre-training Objectives, and Downstream Tasks","created":"2022-01-24","cited_by_count":42,"area":null},{"id":"2104.08303","title":"Capturing Row and Column Semantics in Transformer Based Question Answering over Tables","created":"2021-04-16","cited_by_count":31,"area":null},{"id":"2105.01736","title":"Retrieving Complex Tables with Multi-Granular Graph Representation Learning","created":"2021-05-04","cited_by_count":24,"area":null},{"id":"2310.07875","title":"TabLib: A Dataset of 627M Tables with Context","created":"2023-10-11","cited_by_count":23,"area":null},{"id":"2102.11903","title":"Neural ranking models for document retrieval","created":"2021-02-23","cited_by_count":16,"area":null},{"id":"2108.04049","title":"Multi-modal Retrieval of Tables and Texts Using Tri-encoder Models","created":"2021-08-09","cited_by_count":11,"area":null},{"id":"2205.09843","title":"Table Retrieval May Not Necessitate Table-specific Model Design","created":"2022-05-19","cited_by_count":11,"area":null},{"id":"2106.04441","title":"CLTR: An End-to-End, Transformer-Based System for Cell Level Table Retrieval and Table Question Answering","created":"2021-06-08","cited_by_count":9,"area":null},{"id":"2302.11777","title":"Embeddings for Tabular Data: A Survey","created":"2023-02-23","cited_by_count":2,"area":null},{"id":"2309.10506","title":"Enhancing Open-Domain Table Question Answering via Syntax- and Structure-aware Dense Retrieval","created":"2023-09-19","cited_by_count":2,"area":null},{"id":"2104.14914","title":"BERT Meets Relational DB: Contextual Representations of Relational Databases","created":"2021-04-30","cited_by_count":1,"area":null},{"id":"2603.19888","title":"Integrating Meta-Features with Knowledge Graph Embeddings for Meta-Learning","created":"2026-03-20","cited_by_count":0,"area":null},{"id":"2605.00400","title":"FollowTable: A Benchmark for Instruction-Following Table Retrieval","created":"2026-05-01","cited_by_count":0,"area":null},{"id":"2605.18199","title":"PIPER: Content-Based Table Search via profiling and LLM-Generated Pseudoqueries","created":"2026-05-18","cited_by_count":0,"area":null}],"next_cursor":null}