Regularization of Distinct Strategies for Unsupervised Question Generation
Junmo Kang, Giwon Hong, Haritz Puerto San Roman, Sung-Hyon Myaeng · 2020
Unsupervised question answering (UQA) has been proposed to avoid the high cost of creating high-quality datasets for QA.One approach to UQA is to train a QA model with questions generated automatically.However, the generated questions are either too similar to a word sequence in the context or too drifted from the semantics of the context, thereby making it difficult to train a robust QA model.We propose a novel regularization method based on teacher-student architecture to avoid bias toward a particular question generation strategy and modulate the process of generating individual words when a question is generated.Our experiments demonstrate that we have achieved the goal of generating higher-quality questions for UQA across diverse QA datasets and tasks.We also show that this method can be useful for creating a QA model with few-shot learning.