DoT: An efficient Double Transformer for NLP tasks with tables

1 June 2021

Papers citing "DoT: An efficient Double Transformer for NLP tasks with tables"

8 / 8 papers shown

Title
TableLoRA: Low-rank Adaptation on Table Structure Understanding for Large Language Models Xinyi He Yihao Liu Mengyu Zhou Yeye He Haoyu Dong Shi Han Zejian Yuan Dongmei Zhang LMTD 151 1 0 06 Mar 2025
Training Table Question Answering via SQL Query Decomposition Raphael Mouravieff Benjamin Piwowarski Sylvain Lamprier ReLM LMTD 100 1 0 19 Feb 2024
CABINET: Content Relevance based Noise Reduction for Table Question Answering Sohan Patnaik Heril Changwal Milan Aggarwal Sumita Bhatia Yaman Kumar Balaji Krishnamurthy LMTD RALM 119 19 0 02 Feb 2024
Around the GLOBE: Numerical Aggregation Question-Answering on Heterogeneous Genealogical Knowledge Graphs with Deep Neural Networks Omri Suissa M. Zhitomirsky-Geffet Avshalom Elmalech 66 1 0 30 Jul 2023
Table Retrieval May Not Necessitate Table-specific Model Design Zhiruo Wang Zhengbao Jiang Eric Nyberg Graham Neubig LMTD 78 16 0 19 May 2022
TIE: Topological Information Enhanced Structural Reading Comprehension on Web Pages Zihan Zhao Lu Chen Ruisheng Cao Hongshen Xu Xingyu Chen Kai Yu 94 9 0 13 May 2022
Table Pre-training: A Survey on Model Architectures, Pre-training Objectives, and Downstream Tasks Haoyu Dong Zhoujun Cheng Xinyi He Mengyuan Zhou Anda Zhou Fan Zhou Ao Liu Shi Han Dongmei Zhang LMTD 159 65 0 24 Jan 2022
MATE: Multi-view Attention for Table Transformer Efficiency Julian Martin Eisenschlos Maharshi Gor Thomas Müller William W. Cohen LMTD 108 97 0 09 Sep 2021