Content Planning for Neural Story Generation with Aristotelian Rescoring

By Seraphina Goldfarb-Tarrant, Tuhin Chakrabarty, Ralph M. Weischedel, Nanyun Peng. 2020

141 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

Citations in the corpus

ECONET: Effective Continual Pretraining of Language Models for Event Temporal Reasoning (2020, background): “, 2015, 2016, 2017), generating stories (Yao et al., 2019; Goldfarb-Tarrant et al., 2020), and forecasting social events (Li et al.”
Generating Syntactically Controlled Paraphrases without Using Annotated Parallel Pairs (2021, background): “…Dai et al., 2019), tense (Logeswaran et al., 2018), plots (Ammanabrolu et al., 2020; Fan et al., 2019; Tambwekar et al., 2019; Yao et al., 2019; Goldfarb-Tarrant et al., 2019, 2020), societal bias (Wallace et al., 2019; Sheng et al., 2020b,a), and syntax (Iyyer et al., 2018; Goyal and…”
The Perils of Using Mechanical Turk to Evaluate Open-Ended Text Generation (2021, background): “Each paper was published between 2018 and 2020 at ACL, NAACL, or EMNLP, and we exclude papers that use AMT to evaluate more wellestablished generation tasks like machine translation, or summarization.3 Unlike previous surveys of evaluating generated text (Çelikyilmaz et al., 2020; van der Lee et al.”
Moral Stories: Situated Reasoning about Norms, Intents, Actions, and their Consequences (2020, methodology): “The proposed CoE algorithms, on the other hand, are closely related to rescoring methods employed in NLG, including work by (Holtzman et al., 2018; Cho et al., 2019; Gabriel et al., 2019; Hossain et al., 2020; Goldfarb-Tarrant et al., 2020), among others.”
MERMAID: Metaphor Generation with Symbolism and Discriminative Decoding (2021, methodology): “The rescoring model is inspired by Holtz-man et al. (2018); Goldfarb-Tarrant et al. (2020) and detailed as follows.”
InFillmore: Frame-Guided Language Generation with Bidirectional Context (2021, background): “…(Xu et al., 2018; Yao et al., 2019; Goldfarb-Tarrant et al., 2019; Gupta et al., 2019; Rashkin et al., 2020), short summaries (Jain et al., 2017; Chen et al., 2019), verb-argument tuples (Martin et al., 2018), or PropBank predicates and arguments (Fan et al., 2019; Goldfarb-Tarrant et al., 2020).”
STORIUM: A Dataset and Evaluation Platform for Machine-in-the-Loop Story Generation (2020, background): “, 2019) and structured knowledge, either external (Mao et al., 2019; Guan et al., 2020; Goldfarb-Tarrant et al., 2020) or derived (Yao et al.”
A Survey of Controllable Text Generation Using Transformer-based Pre-trained Language Models (2022, background): “Storystructure[32,33,40],storyending[85,132], topic[16,73,141,153],persona[15,76] DatatoText Input: [78],genderbias[28,104],subjectivebias[101], socialbias[4,121],sentimentbias[51],toxicity[62,74]) FormatControl Input:DesiredFormat,prompttext Output:Textinpre-definedformation Format[66,70,120,122]…”
Co-Writing Screenplays and Theatre Scripts with Language Models: Evaluation by Industry Professionals (2022, methodology): “[45] further introduced rescoring methods for character and plot events.”