QNLI
3 papers with code • 0 benchmarks • 0 datasets
Benchmarks
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Most implemented papers
Tell Me How to Ask Again: Question Data Augmentation with Controllable Rewriting in Continuous Space
In this paper, we propose a novel data augmentation method, referred to as Controllable Rewriting based Question Data Augmentation (CRQDA), for machine reading comprehension (MRC), question generation, and question-answering natural language inference tasks.
Abstract Meaning Representation-Based Logic-Driven Data Augmentation for Logical Reasoning
Combining large language models with logical reasoning enhances their capacity to address problems in a robust and reliable manner.
How to Distill your BERT: An Empirical Study on the Impact of Weight Initialisation and Distillation Objectives
To the best of our knowledge, this is the first work comprehensively evaluating distillation objectives in both settings.