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Found 10 papers, 9 papers with code
CHARD: Clinical Health-Aware Reasoning Across Dimensions for Text Generation Models
We motivate and introduce CHARD: Clinical Health-Aware Reasoning across Dimensions, to investigate the capability of text generation models to act as implicit clinical knowledge bases and generate free-flow textual explanations about various health-related conditions across several dimensions.
PINEAPPLE: Personifying INanimate Entities by Acquiring Parallel Personification data for Learning Enhanced generation
To this end, we propose PINEAPPLE: Personifying INanimate Entities by Acquiring Parallel Personification data for Learning Enhanced generation.
PANCETTA: Phoneme Aware Neural Completion to Elicit Tongue Twisters Automatically
Through automatic and human evaluation, as well as qualitative analysis, we show that PANCETTA generates novel, phonetically difficult, fluent, and semantically meaningful tongue twisters.
Retrieve, Caption, Generate: Visual Grounding for Enhancing Commonsense in Text Generation Models
We investigate the use of multimodal information contained in images as an effective method for enhancing the commonsense of Transformer models for text generation.
SAPPHIRE: Approaches for Enhanced Concept-to-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.
A Survey of Data Augmentation Approaches for NLP
In this paper, we present a comprehensive and unifying survey of data augmentation for NLP by summarizing the literature in a structured manner.
NAREOR: The Narrative Reordering Problem
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.
GenAug: Data Augmentation for Finetuning Text Generators
We also examine the relationship between the amount of augmentation and the quality of the generated text.
ALOHA: Artificial Learning of Human Attributes for Dialogue Agents
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.
Keep Calm and Switch On! Preserving Sentiment and Fluency in Semantic Text Exchange
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.
