- The method section looks not self-contained and lacks descriptions of some key components. In particular: * What is Eq.(9) for? Why "the SL is the negative logarithm of a polynomial in \theta" -- where is the "negative logarithm" in Eq.(9)? * Eq.(9) is not practically tractable. It looks its practical implementation is discussed in the "Evaluating the Semantic Loss" part (L.140) which involves the Weighted Model Count (WMC) and knowledge compilation (KC). However, no details about KC are presented. Considering the importance of the component in the whole proposed approach, I feel it's very necessary to clearly present the details and make the approach self-contained. - The proposed approach essentially treats the structured constraints (a logical rule) as part of the discriminator that supervises the training of the generator. This idea looks not new -- one can simply treat the constraints as an energy function and plug it into energy-based GANs (https://arxiv.org/abs/1609.03126). Modeling structured constraints as a GAN discriminator to train the generative model has also been studied in [15] (which also discussed the relation b/w the structured approach with energy-based GANs). Though the authors derive the formula from a perspective of semantic loss, it's unclear what's the exact difference from the previous work? - The paper claims better results in the Molecule generation experiment (Table.3). However, it looks adding the proposed constrained method actually yields lower validity and diversity.
- The paper claims better results in the Molecule generation experiment (Table.3). However, it looks adding the proposed constrained method actually yields lower validity and diversity.