Semantic-based Self-Critical Training For Question Generation
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We present in this work a fully Transformer-based reinforcement learning generator-evaluator architecture for neural question generation. Question generation is a task that consists in generating questions given a context and answer. To improve the quality of the generated question, we came up with a semantic-based self-critical training layout in generator-evaluator architecture, which goes beyond typical maximum likelihood training. Evaluation metrics for language modeling only based on n-gram overlapping do not consider semantic relations between reference and candidate strings. To improve the evaluation step, we assess our model for both n-gram overlap using BLEU and semantically using BERTScore and NUBIA, a novel state-of-the-art evaluation metric for text generation. Question generation could be used in many downstream applications, including in extending question answering datasets, conversational systems, and educational assessment systems.
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