Dual Learning: Theoretical Study and an Algorithmic Extension
Table 4: Estimated Parameters Using Test Data.
['Threshold BLEU', '10', '20']
For a given test dataset, we define the empirical estimate of α, β, and γ as follows. ^α =\# of x|Td12(x)∈T∗12(x),Td21(Td12(x))∈C(x)\# of x|T21(T12(x))∉C(x) ^β =\# of x|Td12(x)∉T∗12(x),Td21(Td12(x))∈C(x)\# of x|T21(T12(x))∉C(x) ^γ = \# of x|Td21(Td12(x))∉C(x)\# of x|T21(T12(x))∉C(x) Ideally η=1. In each cell, the values on the left are for En→De direction and the values on the right are for De→En direction. The high ^γ values indicate that the reconstruction loss is still high after dual learning. Therefore, we believe there exist approaches to improve dual learning and how to further reduce the reconstruction loss is a promising direction.