The goal of text-to-text generation is to make machines express like a human in many
applications such as conversation, summarization, and translation. It is one of the most …

Knowledge in NLP has been a rising trend especially after the advent of large-scale pre-
trained models. Knowledge is critical to equip statistics-based models with common sense …

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

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 …

Retrieval-enhanced methods have become a primary approach in fact verification (FV); it
requires reasoning over multiple retrieved pieces of evidence to verify the integrity of a …

Humans use natural language to compose common concepts from their environment into
plausible, day-to-day scene descriptions. However, such generative commonsense …

In recent years, Large Language Models (LLMs) have shown remarkable performance in
generating human-like text, proving to be a valuable asset across various applications …

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

Causal reasoning is a critical component of human cognition and is required across a range
of question-answering (QA) tasks (such as abductive reasoning, commonsense QA, and …