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Found 6 papers, 3 papers with code
Towards Fairness in Personalized Ads Using Impression Variance Aware Reinforcement Learning
While there are many definitions of fairness that could be applicable in the context of personalized systems, we present a framework which we call the Variance Reduction System (VRS) for achieving more equitable outcomes in Meta's ads systems.
Automated Storytelling via Causal, Commonsense Plot Ordering
In this work, we introduce the concept of soft causal relations as causal relations inferred from commonsense reasoning.
Bringing Stories Alive: Generating Interactive Fiction Worlds
This knowledge graph is then automatically completed utilizing thematic knowledge and used to guide a neural language generation model that fleshes out the rest of the world.
UNO: Uncertainty-aware Noisy-Or Multimodal Fusion for Unanticipated Input Degradation
Specifically, we analyze a number of uncertainty measures, each of which captures a different aspect of uncertainty, and we propose a novel way to fuse degraded inputs by scaling modality-specific output softmax probabilities.
Story Realization: Expanding Plot Events into Sentences
Neural network based approaches to automated story plot generation attempt to learn how to generate novel plots from a corpus of natural language plot summaries.
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Guided Neural Language Generation for Automated Storytelling
Prior work has shown that a semantic abstraction of sentences called events improves neural plot generation and and allows one to decompose the problem into: (1) the generation of a sequence of events (event-to-event) and (2) the transformation of these events into natural language sentences (event-to-sentence).
