INSET: Sentence Infilling with Inter-sentential Generative Pre-training
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Missing sentence generation (or sentence infilling) fosters a wide range of applications in natural language generation, such as document auto-completion and meeting note expansion. Such a task asks the model to generate intermediate missing sentence that can semantically and syntactically bridge the surrounding context. Solving the sentence infilling task requires techniques in NLP ranging from natural language understanding, discourse-level planning and natural language generation. In this paper, we present a framework to decouple this challenge and address these three aspects respectively, leveraging the power of existing large-scale pre-trained models such as BERT and GPT-2. Our empirical results demonstrate the effectiveness of our proposed model in learning a sentence representation for generation, and further generating a missing sentence that bridges the context.
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