Jinqiao Dai, Pengsen Cheng, Jiayong Liu*
CMC-Computers, Materials & Continua, Vol.80, No.1, pp. 547-565, 2024, DOI:10.32604/cmc.2024.051745
Abstract Generating diverse and factual text is challenging and is receiving increasing attention. By sampling from the latent space, variational autoencoder-based models have recently enhanced the diversity of generated text. However, existing research predominantly depends on summarization models to offer paragraph-level semantic information for enhancing factual correctness. The challenge lies in effectively generating factual text using sentence-level variational autoencoder-based models. In this paper, a novel model called fact-aware conditional variational autoencoder is proposed to balance the factual correctness and diversity of generated text. Specifically, our model encodes the input sentences and uses them as facts to… More >
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