Generating Reasonable and Diversified Story Ending Using Sequence to Sequence Model with Adversarial Training

COLING 2018 Zhongyang LiXiao DingTing Liu

Story generation is a challenging problem in artificial intelligence (AI) and has received a lot of interests in the natural language processing (NLP) community. Most previous work tried to solve this problem using Sequence to Sequence (Seq2Seq) model trained with Maximum Likelihood Estimation (MLE)... (read more)

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