We investigate the use of multimodal information contained in images as an effective method for enhancing the commonsense of Transformer models for text generation.
We motivate and propose a suite of simple but effective improvements for concept-to-text generation called SAPPHIRE: Set Augmentation and Post-hoc PHrase Infilling and REcombination.
In this paper, we present a comprehensive and unifying survey of data augmentation for NLP by summarizing the literature in a structured manner.
In this paper, we propose and investigate the task of Narrative Reordering (NAREOR) which involves rewriting a given story in a different narrative order while preserving its plot.
We also examine the relationship between the amount of augmentation and the quality of the generated text.
We propose Human Level Attributes (HLAs) based on tropes as the basis of a method for learning dialogue agents that can imitate the personalities of fictional characters.
In this paper, we present a novel method for measurably adjusting the semantics of text while preserving its sentiment and fluency, a task we call semantic text exchange.