  Embedding commonsense knowledge is crucial for end-to-end models to
generalize inference beyond training corpora. However, existing word analogy
datasets have tended to be handcrafted, involving permutations of hundreds of
words with only dozens of pre-defined relations, mostly morphological relations
and named entities. In this work, we model commonsense knowledge down to
word-level analogical reasoning by leveraging E-HowNet, an ontology that
annotates 88K Chinese words with their structured sense definitions and English
translations. We present CA-EHN, the first commonsense word analogy dataset
containing 90,505 analogies covering 5,656 words and 763 relations. Experiments
show that CA-EHN stands out as a great indicator of how well word
representations embed commonsense knowledge. The dataset is publicly available
at https://github.com/ckiplab/CA-EHN.
