Vector space models of words have long been claimed to capture linguistic
regularities as simple vector translations, but problems have been raised with
this claim. We decompose and empirically analyze the classic arithmetic word
analogy test, to motivate two new metrics that address the issues with the
standard test, and which distinguish between class-wise offset concentration
(similar directions between pairs of words drawn from different broad classes,
such as France--London, China--Ottawa, ...) and pairing consistency (the
existence of a regular transformation between correctly-matched pairs such as
France:Paris::China:Beijing). We show that, while the standard analogy test is
flawed, several popular word embeddings do nevertheless encode linguistic
regularities.

本文通过分解和实证分析经典算数词类比测试，提出了两种新的指标，用于解决标准测试存在的问题，并区分广泛类别内二元词汇之间的相似方向和正确匹配词对之间存在常规变换的配对一致性；结果表明，尽管标准的词类比测试存在缺陷，但一些受欢迎的词嵌入方法仍然编码语言规律。