Reflection-based Word Attribute Transfer
Table 2: Results in accuracy and stability scores: MF, SP, CC, and AN are datasets.
['[BOLD] Method', '[BOLD] Knowledge', '[BOLD] word2vec  [BOLD] Accuracy (%)', '[BOLD] word2vec  [BOLD] Accuracy (%)', '[BOLD] word2vec  [BOLD] Accuracy (%)', '[BOLD] word2vec  [BOLD] Accuracy (%)', '[BOLD] word2vec  [BOLD] Stability (%)', '[BOLD] word2vec  [BOLD] Stability (%)', '[BOLD] word2vec  [BOLD] Stability (%)', '[BOLD] word2vec  [BOLD] Stability (%)', '[BOLD] GloVe  [BOLD] Accuracy (%)', '[BOLD] GloVe  [BOLD] Accuracy (%)', '[BOLD] GloVe  [BOLD] Accuracy (%)', '[BOLD] GloVe  [BOLD] Accuracy (%)', '[BOLD] GloVe  [BOLD] Stability (%)', '[BOLD] GloVe  [BOLD] Stability (%)', '[BOLD] GloVe  [BOLD] Stability (%)', '[BOLD] GloVe  [BOLD] Stability (%)']
Different pre-trained word embeddings GloVe or word2vec gave similar results. Ref+PM achieved the best accuracy among the methods that did not use explicit attribute knowledge. For example, the accuracy of Ref+PM was 76% in Capital-Country, but the accuracy of Diff+ was 26%. For stability, reflection-based transfers achieved outstanding stability scores that exceeded 99%. The results show that our proposed method transfers an input word if it has a target attribute and does not transfer an input word with better score than the baselines otherwise, even though the proposed method does not use attribute knowledge of the input words. MLP worked poorly both in accuracy and stability. On the antonym dataset, although the transfer accuracy by the proposed method was a bit lower than that by MLP, the proposed method ’s stability was 100% and that of MLP was extremely poor: about 1%.