7 citations · 15 across the 4 of their papers we have counts for
6 papers
Exploring Generalization Ability of Pretrained Language Models on Arithmetic and Logical Reasoning
Cunxiang Wang, Boyuan Zheng, Yuchen Niu +1
To quantitatively and intuitively explore the generalization ability of pre-trained language models (PLMs), we have designed several tasks of arithmetic and logical reasoning. We b…
Can Generative Pre-trained Language Models Serve as Knowledge Bases for Closed-book QA?
Cunxiang Wang, Pai Liu, Yue Zhang
Recent work has investigated the interesting question using pre-trained language models (PLMs) as knowledge bases for answering open questions. However, existing work is limited in…
Commonsense Knowledge Graph Reasoning by Selection or Generation? Why?
Cunxiang Wang, Jinhang Wu, Luxin Liu +1
Commonsense knowledge graph reasoning(CKGR) is the task of predicting a missing entity given one existing and the relation in a commonsense knowledge graph (CKG). Existing methods…
SemEval-2020 Task 4: Commonsense Validation and Explanation
Cunxiang Wang, Shuailong Liang, Yili Jin +3
In this paper, we present SemEval-2020 Task 4, Commonsense Validation and Explanation (ComVE), which includes three subtasks, aiming to evaluate whether a system can distinguish a…
Does It Make Sense? And Why? A Pilot Study for Sense Making and Explanation
Cunxiang Wang, Shuailong Liang, Yue Zhang +2
Introducing common sense to natural language understanding systems has received increasing research attention. It remains a fundamental question on how to evaluate whether a system…
Domain Representation for Knowledge Graph Embedding
Cunxiang Wang, Feiliang Ren, Zhichao Lin +3
Embedding entities and relations into a continuous multi-dimensional vector space have become the dominant method for knowledge graph embedding in representation learning. However,…