From Plots to Endings: A Reinforced Pointer Generator for Story Ending Generation
This addresses story ending generation for narrative AI applications, representing an incremental advance with specific gains in coherence and relevance.
The paper tackles the task of generating coherent story endings from plots by proposing a reinforced pointer generator framework, achieving improvements of 15.75% in CIDEr and 13.57% in consistency score over a baseline model.
We introduce a new task named Story Ending Generation (SEG), whic-h aims at generating a coherent story ending from a sequence of story plot. Wepropose a framework consisting of a Generator and a Reward Manager for thistask. The Generator follows the pointer-generator network with coverage mech-anism to deal with out-of-vocabulary (OOV) and repetitive words. Moreover, amixed loss method is introduced to enable the Generator to produce story endingsof high semantic relevance with story plots. In the Reward Manager, the rewardis computed to fine-tune the Generator with policy-gradient reinforcement learn-ing (PGRL). We conduct experiments on the recently-introduced ROCStoriesCorpus. We evaluate our model in both automatic evaluation and human evalua-tion. Experimental results show that our model exceeds the sequence-to-sequencebaseline model by 15.75% and 13.57% in terms of CIDEr and consistency scorerespectively.