- Missing ablations: It is unclear from the results how much performance gain is due to the task formulation, and how much is because of pre-trained language models. The paper should include results using the GCPG model without pre-trained initializations. - Missing baselines for lexically controlled paraphrasing: The paper does not compare with any lexically constrained decoding methods (see references below). Moreover, the keyword control mechanism proposed in this method has been introduced in CTRLSum paper (He et al, 2020) for keywork-controlled summarization.
- Related to the point above, the related work section is severely lacking (see below for missing references). Particularly, the paper completely omits lexically constrained decoding methods, both in related work and as baselines for comparison.
- The paper is hard to follow and certain sections (particularly the Experimental Setup) needs to made clearer. It was tough to understand exactly where the exemplar/target syntax was obtained for different settings, and how these differed between training and inference for each of those settings.
- The paper should include examples of generated paraphrases using all control options studies (currently only exemplar-controlled examples are included in Figure 5). Also, including generation from baseline systems for the same examples would help illustrate the differences better. Missing References: Syntactically controlled paraphrase generation: Goyal et al., ACL2020, Neural Syntactic Preordering for Controlled Paraphrase Generation Sun et al. EMNLP2021, AESOP: Paraphrase Generation with Adaptive Syntactic Control <-- this is a contemporaneous work, but would be nice to cite in next version. Keyword-controlled decoding strategies: Hokamp et al. ACL2017, Lexically Constrained Decoding for Sequence Generation Using Grid Beam Search Post et al, NAACL 2018, Fast Lexically Constrained Decoding with Dynamic Beam Allocation for Neural Machine Translation Other summarization work that uses similar technique for keyword control: He et al, CTRLSum, Towards Generic Controllable Text Summarization
- Missing ablations: It is unclear from the results how much performance gain is due to the task formulation, and how much is because of pre-trained language models. The paper should include results using the GCPG model without pre-trained initializations.