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Found 7 papers, 3 papers with code
Text2Analysis: A Benchmark of Table Question Answering with Advanced Data Analysis and Unclear Queries
We evaluate five state-of-the-art models using three different metrics and the results show that our benchmark presents introduces considerable challenge in the field of tabular data analysis, paving the way for more advanced research opportunities.
FORTAP: Using Formulas for Numerical-Reasoning-Aware Table Pretraining
In this paper, we find that the spreadsheet formula, which performs calculations on numerical values in tables, is naturally a strong supervision of numerical reasoning.
HiTab: A Hierarchical Table Dataset for Question Answering and Natural Language Generation
HiTab provides 10, 686 QA pairs and descriptive sentences with well-annotated quantity and entity alignment on 3, 597 tables with broad coverage of table hierarchies and numerical reasoning types.
TabularNet: A Neural Network Architecture for Understanding Semantic Structures of Tabular Data
To simultaneously extract spatial and relational information from tables, we propose a novel neural network architecture, TabularNet.
TUTA: Tree-based Transformers for Generally Structured Table Pre-training
First, we devise a unified tree-based structure, called a bi-dimensional coordinate tree, to describe both the spatial and hierarchical information of generally structured tables.
Improved Relation Classification by Deep Recurrent Neural Networks with Data Augmentation
However, existing neural networks for relation classification are usually of shallow architectures (e. g., one-layer convolutional neural networks or recurrent networks).
Ranked #2 on
Relation Classification
on SemEval 2010 Task 8
Distilling Word Embeddings: An Encoding Approach
Distilling knowledge from a well-trained cumbersome network to a small one has recently become a new research topic, as lightweight neural networks with high performance are particularly in need in various resource-restricted systems.
