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Found 7 papers, 5 papers with code
Document Understanding Dataset and Evaluation (DUDE)
We call on the Document AI (DocAI) community to reevaluate current methodologies and embrace the challenge of creating more practically-oriented benchmarks.
STable: Table Generation Framework for Encoder-Decoder Models
The output structure of database-like tables, consisting of values structured in horizontal rows and vertical columns identifiable by name, can cover a wide range of NLP tasks.
Joint Entity and Relation Extraction
Knowledge Base Population
Going Full-TILT Boogie on Document Understanding with Text-Image-Layout Transformer
We address the challenging problem of Natural Language Comprehension beyond plain-text documents by introducing the TILT neural network architecture which simultaneously learns layout information, visual features, and textual semantics.
Ranked #1 on
on
From Dataset Recycling to Multi-Property Extraction and Beyond
This paper investigates various Transformer architectures on the WikiReading Information Extraction and Machine Reading Comprehension dataset.
Successive Halving Top-k Operator
We propose a differentiable successive halving method of relaxing the top-k operator, rendering gradient-based optimization possible.
Sparsifying Transformer Models with Trainable Representation Pooling
A reduction of quadratic time and memory complexity to sublinear was achieved due to a robust trainable top-$k$ operator.
Ranked #2 on
Text Summarization
on arXiv Summarization Dataset
On the Multi-Property Extraction and Beyond
In this paper, we investigate the Dual-source Transformer architecture on the WikiReading information extraction and machine reading comprehension dataset.
