CommonsenseKnowledgeGraph

AbstractIdentifying what is at the center of the meaning of a word and what discriminates it from other words is a fundamental natural language inference task. This paper describes an …...

AbstractCommonsense question answering (QA) requires background knowledge which is not explicitly stated in a given context. Prior works use commonsense knowledge graphs (KGs) to obtain this knowledge for reasoning. However, …...

AbstractHuman conversations naturally evolve around related concepts and hop to distant concepts. This paper presents a new conversation generation model, ConceptFlow, which leverages commonsense knowledge graphs to explicitly model conversation …...

Inspired by evidence that pretrained language models (LMs) encode commonsense knowl- edge, recent work has applied LMs to auto- matically populate commonsense knowledge graphs (CKGs). However, there is a lack …...

AbstractThere is an increasing interest in the use of mathematical word problem (MWP) generation in educational assessment. Different from standard natural question generation, MWP generation needs to maintain the underlying …...

AbstractKnowledge Graph (KG) completion research usually focuses on densely connected benchmark datasets that are not representative of real KGs. We curate two KG datasets that include biomedical and encyclopedic knowledge …...

AbstractCommonsense question answering (QA) requires machines to utilize the QA content and external commonsense knowledge graph (KG) for reasoning when answering questions. Existing work uses two independent modules to model …...

AbstractGenerative commonsense reasoning (GCR) in natural language is to reason about the commonsense while generating coherent text. Recent years have seen a surge of interest in improving the generation quality …...

AbstractThe common practice for training commonsense models has gone from–human–to–corpus–to–machine: humans author commonsense knowledge graphs in order to train commonsense models. In this work, we investigate an alternative, from–machine–to–corpus–to–machine: general …...

AbstractATOMIC is a large-scale commonsense knowledge graph (CSKG) containing everyday if-then knowledge triplets, i.e., head event, relation, tail event. The one-hop annotation manner made ATOMIC a set of independent bipartite …...

AbstractHuman conversations contain natural and reasonable topic shifts, reflected as the concept flows across utterances. Previous researches prove that explicitly modeling concept flows with a large commonsense knowledge graph effectively …...