Thank reviewers for detailed comments. Our main contribution is the novel search-and-learning for1 UnsupTextGen, achieving remarkable performance (sometimes even better than SupTextGen). Forexample,8 the rules/heuristics in [18] only applies to sentiment style transfer.Future work MT: Thanks for9 suggesting the future work. We are currently considering MT by using word-level dictionary and10 performingsearchandlearning. R2: 3(Novelty): Our main novelty is the search-and-learning framework TGSL for UnsupTextGen,16 where our learning is non-trivial and involves two stages with different losses, well motivated and17 supportedbyablationstudy.

Thank reviewers for detailed comments. By "generic" we mean the model can be applied to different tasks that share the same problem structure. We're happy to revise the terminology and highlight what applications TGSL is appropriate for, namely, those where the input and output show certain resemblance. We mentioned in Line 269 that SA+MM cannot achieve reasonable performance. To our best knowledge, we are the first to work in this direction.