 To solve this , we propose to separate word importance and word meaning by decomposing word vectors into their norm and direction , then compute the alignment-based similarity with the help of earth mover's distance . We call the method word rotator's distance (WRD) because direction vectors are aligned by rotation on the unit hypersphere.
<meaning-changed>  To solve this , we propose to separate word importance and word meaning by decomposing word vectors into their norm and direction , then compute the alignment-based similarity with the help of earth mover's distance . We call the method word rotator's distance (WRD) because direction vectors are aligned by rotation on the unit hypersphere.
 To solve this , we propose to separate word importance and word meaning by decomposing word vectors into their norm and direction , then compute the alignment-based similarity with the help of earth mover's distance (optimal transport cost), which we refer to as word rotator's distance (WRD) because direction vectors are aligned by rotation on the unit hypersphere.