4 citations · 5 across the 4 of their papers we have counts for
9 papers · 1 filter
Zero and Few-shot Learning for Author Profiling
Mara Chinea-Rios, Thomas Müller, Gretel Liz De la Peña Sarracén +2
Author profiling classifies author characteristics by analyzing how language is shared among people. In this work, we study that task from a low-resource viewpoint: using little or…
Active Few-Shot Learning with FASL
Thomas Müller, Guillermo Pérez-Torró, Angelo Basile +1
Recent advances in natural language processing (NLP) have led to strong text classification models for many tasks. However, still often thousands of examples are needed to train mo…
Few-Shot Learning with Siamese Networks and Label Tuning
Thomas Müller, Guillermo Pérez-Torró, Marc Franco-Salvador
We study the problem of building text classifiers with little or no training data, commonly known as zero and few-shot text classification. In recent years, an approach based on ne…
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…