2 citations · 2 across the 4 of their papers we have counts for
4 papers
Leveraging Data Recasting to Enhance Tabular Reasoning
Aashna Jena, Vivek Gupta, Manish Shrivastava +1
Creating challenging tabular inference data is essential for learning complex reasoning. Prior work has mostly relied on two data generation strategies. The first is human annotati…
SLATE: A Sequence Labeling Approach for Task Extraction from Free-form Inked Content
Apurva Gandhi, Ryan Serrao, Biyi Fang +8
We present SLATE, a sequence labeling approach for extracting tasks from free-form content such as digitally handwritten (or "inked") notes on a virtual whiteboard. Our approach al…
Realistic Data Augmentation Framework for Enhancing Tabular Reasoning
Dibyakanti Kumar, Vivek Gupta, Soumya Sharma +1
Existing approaches to constructing training data for Natural Language Inference (NLI) tasks, such as for semi-structured table reasoning, are either via crowdsourcing or fully aut…
Enhancing Tabular Reasoning with Pattern Exploiting Training
Abhilash Reddy Shankarampeta, Vivek Gupta, Shuo Zhang
Recent methods based on pre-trained language models have exhibited superior performance over tabular tasks (e.g., tabular NLI), despite showing inherent problems such as not using…