4 citations · 11 across the 3 of their papers we have counts for
9 papers
Faithful Chart Summarization with ChaTS-Pi
Syrine Krichene, Francesco Piccinno, Fangyu Liu +1
Chart-to-summary generation can help explore data, communicate insights, and help the visually impaired people. Multi-modal generative models have been used to produce fluent summa…
Table-To-Text generation and pre-training with TabT5
Ewa Andrejczuk, Julian Martin Eisenschlos, Francesco Piccinno +2
Encoder-only transformer models have been successfully applied to different table understanding tasks, as in TAPAS (Herzig et al., 2020). A major limitation of these architectures…
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…
Understanding tables with intermediate pre-training
Julian Martin Eisenschlos, Syrine Krichene, Thomas Müller
Table entailment, the binary classification task of finding if a sentence is supported or refuted by the content of a table, requires parsing language and table structure as well a…