4 research outputs found
Learning to Predict Explainable Plots for Neural Story Generation
Story generation is an important natural language processing task that aims
to generate coherent stories automatically. While the use of neural networks
has proven effective in improving story generation, how to learn to generate an
explainable high-level plot still remains a major challenge. In this work, we
propose a latent variable model for neural story generation. The model treats
an outline, which is a natural language sentence explainable to humans, as a
latent variable to represent a high-level plot that bridges the input and
output. We adopt an external summarization model to guide the latent variable
model to learn how to generate outlines from training data. Experiments show
that our approach achieves significant improvements over state-of-the-art
methods in both automatic and human evaluations.Comment: 10 page
InFillmore: Frame-Guided Language Generation with Bidirectional Context
We propose a structured extension to bidirectional-context conditional
language generation, or "infilling," inspired by Frame Semantic theory
(Fillmore, 1976). Guidance is provided through two approaches: (1) model
fine-tuning, conditioning directly on observed symbolic frames, and (2) a novel
extension to disjunctive lexically constrained decoding that leverages frame
semantic lexical units. Automatic and human evaluations confirm that
frame-guided generation allows for explicit manipulation of intended infill
semantics, with minimal loss in distinguishability from human-generated text.
Our methods flexibly apply to a variety of use scenarios, and we provide a
codebase and interactive demo available from
https://nlp.jhu.edu/demos/infillmore.Comment: Appearing in *SEM 202
Narrative Text Generation with a Latent Discrete Plan
Past work on story generation has demonstrated the usefulness of conditioning
on a generation plan to generate coherent stories. However, these approaches
have used heuristics or off-the-shelf models to first tag training stories with
the desired type of plan, and then train generation models in a supervised
fashion. In this paper, we propose a deep latent variable model that first
samples a sequence of anchor words, one per sentence in the story, as part of
its generative process. During training, our model treats the sequence of
anchor words as a latent variable and attempts to induce anchoring sequences
that help guide generation in an unsupervised fashion. We conduct experiments
with several types of sentence decoder distributions: left-to-right and
non-monotonic, with different degrees of restriction. Further, since we use
amortized variational inference to train our model, we introduce two
corresponding types of inference network for predicting the posterior on anchor
words. We conduct human evaluations which demonstrate that the stories produced
by our model are rated better in comparison with baselines which do not
consider story plans, and are similar or better in quality relative to
baselines which use external supervision for plans. Additionally, the proposed
model gets favorable scores when evaluated on perplexity, diversity, and
control of story via discrete plan.Comment: Findings of EMNLP 202
Content Planning for Neural Story Generation with Aristotelian Rescoring
Long-form narrative text generated from large language models manages a
fluent impersonation of human writing, but only at the local sentence level,
and lacks structure or global cohesion. We posit that many of the problems of
story generation can be addressed via high-quality content planning, and
present a system that focuses on how to learn good plot structures to guide
story generation. We utilize a plot-generation language model along with an
ensemble of rescoring models that each implement an aspect of good
story-writing as detailed in Aristotle's Poetics. We find that stories written
with our more principled plot-structure are both more relevant to a given
prompt and higher quality than baselines that do not content plan, or that plan
in an unprincipled way.Comment: EMNLP 2020, 9 page