3 papers with code • 1 benchmarks • 2 datasets

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Most implemented papers

Tell Me How to Ask Again: Question Data Augmentation with Controllable Rewriting in Continuous Space

dayihengliu/CRQDA EMNLP 2020

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

strong-ai-lab/logical-equivalence-driven-amr-data-augmentation-for-representation-learning 21 May 2023

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

mainlp/how-to-distill-your-bert 24 May 2023

To the best of our knowledge, this is the first work comprehensively evaluating distillation objectives in both settings.