Reflection-based Word Attribute Transfer
Table 3: Relation among size of |Ntrain| and stability of learning-based methods
['[EMPTY]', '[EMPTY]', '[BOLD] Accuracy (%) |Ntrain|', '[BOLD] Accuracy (%) |Ntrain|', '[BOLD] Accuracy (%) |Ntrain|', '[BOLD] Accuracy (%) |Ntrain|', '[BOLD] Stability (%) |Ntrain|', '[BOLD] Stability (%) |Ntrain|', '[BOLD] Stability (%) |Ntrain|', '[BOLD] Stability (%) |Ntrain|']
We investigated the relation between the training data size of the non-attribute words, and the stability of the learning-based methods by conducting an additional experiment that varied |Ntrain|. On the other hand, Ref+PM achieved high stability scores with |Ntrain|=0 and maintained the accuracy. We hypothesized that the high stability came from the distance between the word and its mirror. If non-attribute words are distributed on the mirror, they will not be transferred. The result shows that non-attribute words are close to the mirror, and attribute words are distributed away from it. In Male-Female and Singular-Plural, the distance is not significantly farther than Antonym and Capital-Country. If the distance between paired words is very small, the distance between the word and its mirror is also small. Fig. The distance of Male-Female and Singular-Plural is much smaller than Capital-Country and Antonym.