Weaknesses: My primary concern with this paper is that the problem it is addressing is *extremely* niche --- Modulo cameras are a somewhat obscure problem even within the realm of the computational imaging community. If I was reviewing this paper for a computational imaging/photography conference, I would be more charitable towards this paper. But this subject is unlikely to be of interest to the general NeurIPS audience, and this paper seems unlikely to reach its intended audience if presented at NeurIPS. And the specifics of this neural network architecture are so specifically tailored to this particular problem that I'm not sure what a general ML researcher could come away from this paper with, nor am I convinced that this is a problem that should be popularized with ML researchers as, again, a solution to this problem has limited practical value given that modulo cameras are still a largely hypothetical concept. My other concern with this paper (which would be a significant concern even if I were reviewing this paper in a computational imaging conference) is that the baseline evaluation is misleading.

The datasets are split into two task types, word intrusion and word analogy, and contain 31362 task units in total. The characteristics of the tasks and datasets are that they build upon small, domain-specific corpora, and that the datasets contain a high number of named entities. The datasets were created manually for two fantasy novel book series ("A Song of Ice and Fire" and "Harry Potter"). We provide baseline evaluations with popular word embedding models trained on the book corpora for the given tasks, both for the Russian and English language versions of the datasets. Finally, we compare and analyze the results and discuss specifics of Russian language with regards to the problem setting.