A Hybrid Model for Globally Coherent Story Generation

Abstract

Automatically generating globally coherent stories is a challenging problem. Neural text generation models have been shown to perform well at generating fluent sentences from data, but they usually fail to keep track of the overall coherence of the story after a couple of sentences. Existing work that incorporates a text planning module succeeded in generating recipes and dialogues, but appears quite datademanding. We propose a novel story generation approach that generates globally coherent stories from a fairly small corpus. The model exploits a symbolic text planning module to produce text plans, thus reducing the demand of data; a neural surface realization module then generates fluent text conditioned on the text plan. Human evaluation showed that our model outperforms various baselines by a wide margin and generates stories which are fluent as well as globally coherent.

Story Generation with Rich Details

“…Baselines We evaluate our model against three baselines: (1) the GRU-based model from Zhai et al (2019), as it also takes an ordered agenda as input;…”

“…Other external story generation systems we considered are not suitable either because they do not admit a compatible input format (i.e. taking a linear agenda as input) or that they cannot fully exploit the temporal order information encoded in an linear agenda (such systems tend to perform poorly on INSCRIPT based story generation (see Zhai et al (2019)).…”

“…Fan et al (2018), Fan et al (2019)), a list of events (see, e.g. Zhai et al (2019), Martin et al (2018), Ammanabrolu et al (2019)) or entities (see, e.g. Kiddon et al (2016), Clark et al (2018)) etc., to guide the generation process.…”

“…Figure 1: A story about grocery shopping generated by Zhai et al (2019), which is globally coherent but really boring. Figure 1 shows a story about grocery shopping.…”

Facts2Story: Controlling Text Generation by Key Facts

“…Kiddon et al (2016) avoid the need to represent each of the desired sentences by maintaining a checklist of required words and implementing a gating mechanism to insert these words and track which were used, demonstrating these abilities on cooking recipes. Zhai et al (2019) develop this notion further by using events as the ingredients in the checklist, but also conditioning the generated text on the desired next event, concluding that their model still generates shorter stories with less event coverage then those produced by humans. Fan et al (2018) collect a corpus of writing prompts and their appropriate stories as generated by Reddit users.…”