Generating Sentences from Disentangled Syntactic and Semantic Spaces
Table 4: Performance of syntax-transfer generation. The larger↑ (or lower↓), the better. The results of VAE are obtained by averaging interpolation. Δword-BLEU=word-BLEU(Refsem)−word-% BLEU(Refsyn). We also compute the difference as ΔTED=TED(Refsem)−TED(Refsyn) to measure if the generated sentence is syntactically similar to Refsyn but not Refsem. Due to the difference of scale between BLEU and TED, we compute the geometric mean of Δword-BLEU and ΔTED reflect the total differences.
['Model', 'word-BLEU (corpus) Refsem↑', 'word-BLEU (corpus) Refsyn↓', 'Δword-BLEU↑', 'Average TED (per sentence) Refsem↑', 'Average TED (per sentence) Refsyn↓', 'ΔTED↑', 'Geo Mean Δ↑']
S4SS3SSS0Px2 Results This is because Refsyn and Refsem—even if we artificially split the latent space into two parts—play the same role in the decoder. With the multi-task and adversarial losses for syntactic and semantic latent spaces, the total difference is increased by 12.09, which shows the success of syntax-transfer sentence generation. This further implies that explicitly modeling syntax is feasible in the latent space of VAE.