Commonsense generation is a challenging task of generating a plausible sentence
describing an everyday scenario using provided concepts. Its requirement of reasoning over …

Generative commonsense reasoning requires machines to generate sentences describing
an everyday scenario given several concepts, which has attracted much attention recently …

Recently, large-scale pre-trained language models have demonstrated impressive
performance on several commonsense-reasoning benchmark datasets. However, building …

Commonsense generation aims to generate a realistic sentence describing a daily scene
under the given concepts, which is very challenging, since it requires models to have …

Pre-trained language models have led to substantial gains over a broad range of natural
language processing (NLP) tasks, but have been shown to have limitations for natural …

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 …

Commonsense generation aims at generating plausible everyday scenario description
based on a set of provided concepts. Digging the relationship of concepts from scratch is …

Generative commonsense reasoning which aims to empower machines to generate
sentences with the capacity of reasoning over a set of concepts is a critical bottleneck for text …

In this paper, we propose DimonGen, which aims to generate diverse sentences describing
concept relationships in various everyday scenarios. To support this, we first create a …

Recent advances in commonsense reasoning depend on large-scale human-annotated
training data to achieve peak performance. However, manual curation of training examples …