42 citations · 47 across the 5 of their papers we have counts for
4 papers · 1 filter
MATE: Multi-view Attention for Table Transformer Efficiency
Julian Martin Eisenschlos, Maharshi Gor, Thomas Müller +1
This work presents a sparse-attention Transformer architecture for modeling documents that contain large tables. Tables are ubiquitous on the web, and are rich in information. Howe…
DoT: An efficient Double Transformer for NLP tasks with tables
Syrine Krichene, Thomas Müller, Julian Martin Eisenschlos
Transformer-based approaches have been successfully used to obtain state-of-the-art accuracy on natural language processing (NLP) tasks with semi-structured tables. These model arc…
TAPAS at SemEval-2021 Task 9: Reasoning over tables with intermediate pre-training
Thomas Müller, Julian Martin Eisenschlos, Syrine Krichene
We present the TAPAS contribution to the Shared Task on Statement Verification and Evidence Finding with Tables (SemEval 2021 Task 9, Wang et al. (2021)). SEM TAB FACT Task A is a…
Open Domain Question Answering over Tables via Dense Retrieval
Jonathan Herzig, Thomas Müller, Syrine Krichene +1
Recent advances in open-domain QA have led to strong models based on dense retrieval, but only focused on retrieving textual passages. In this work, we tackle open-domain QA over t…