Towards a Model-Theoretic View of Narratives

Castricato, Louis; Biderman, Stella; Thue, David; Cardona-Rivera, Rogelio E.

doi:10.18653/v1/2021.nuse-1.10

Cited by 2 publications

(1 citation statement)

References 18 publications

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“…Question-answering protocols, wherein questions are asked about a story, have been proposed as a means to make human-participant evaluations more objective (Riedl and Young 2010;Cardona-Rivera et al 2016). We conduct a human-participant evaluation using a metric based on the Fabula Entropy Indices (Castricato et al 2021b), a set of objective measures of story coherence grounded in narratological formalisms (Castricato et al 2021a). The Fabula Entropy Indices are validated to be correlated with more common, subjective evaluation metrics, which we also replicate in this paper to provide further experimental evidence.…”

Section: Objective Evaluationmentioning

confidence: 99%

Automated Story Generation as Question-Answering

Castricato¹,

Frazier²,

Balloch³

et al. 2021

Preprint

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Neural language model-based approaches to automated story generation suffer from two important limitations. First, language model-based story generators generally do not work toward a given goal or ending. Second, they often lose coherence as the story gets longer. We propose a novel approach to automated story generation that treats the problem as one of generative question-answering. Our proposed story generation system starts with sentences encapsulating the final event of the story. The system then iteratively (1) analyzes the text describing the most recent event, (2) generates a question about "why" a character is doing the thing they are doing in the event, and then (3) attempts to generate another, preceding event that answers this question. We evaluate the coherence of generated stories using human participant studies that show that stories generated by our system are 15% more coherent (p < 0.013) than those generated by BART fine-tuned on a story corpus to generate backward from a given ending.

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Section: Objective Evaluationmentioning

confidence: 99%

Automated Story Generation as Question-Answering

Castricato¹,

Frazier²,

Balloch³

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

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On weak filters and ultrafilters: Set theory from (and for) knowledge representation

Koutras

Moyzes

Nomikos

et al. 2021

Logic Journal of the IGPL

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Weak filters were introduced by K. Schlechta in the ’90s with the aim of interpreting defaults via a generalized ‘most’ quantifier in first-order logic. They arguably represent the largest class of structures that qualify as a ‘collection of large subsets’ of a given index set $I$, in the sense that it is difficult to think of a weaker, but still plausible, definition of the concept. The notion of weak ultrafilter naturally emerges and has been used in epistemic logic and other knowledge representation (KR) applications. We provide a comprehensive exposition of weak filters and ultrafilters, comparing them with their classical counterparts that have found very important applications in logic, set theory and topology. Weak (ultra)filters capture the ‘majorities’ of social choice theory (which coincide with the commonsense understanding of a ‘large subset’) and in that respect, they outperform classical (ultra)filters in their role as ‘collections of large subsets’. Yet, they lack some of the elegant properties of classical (ultra)filters that make them an almost perfect match for logical theories. We investigate the extent to which some important classical results carry through in this new setting, and we focus on genuinely weak filters and genuinely weak ultrafilters. For weak ultrafilters, we proceed to provide concrete examples, answer questions of existence and characterize their construction. The class of weak (ultra)filters represents a genuine contribution of KR to set theory that might be of some interest to set theorists too and we initiate this study by providing a glimpse on natural set-theoretic questions.

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Towards a Model-Theoretic View of Narratives

Cited by 2 publications

References 18 publications

Automated Story Generation as Question-Answering

Automated Story Generation as Question-Answering

On weak filters and ultrafilters: Set theory from (and for) knowledge representation

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