Review for NeurIPS paper: Sequence to Multi-Sequence Learning via Conditional Chain Mapping for Mixture Signals

Weaknesses: Generality: The idea is not as general as has been presented. It is quite similar to techniques like multisource decoding [1], where the decoder of a network is conditioned on multiple input sequences, and deliberation models [2]. Furthermore, if one uses attention based models that directly model p(Y X), conditioning multiple sequences is quite straightforward, and not uncommon. For example, if there are multiple sequences Y1, Y1, by concatenating Y1 and Y2, one can model p(Y1, Y2 X) P(Y1 X) P(Y2 Y1, X), which naturally conditions on both input and prior sequences (see, e.g., [4]). The novelty lies mostly in how this is being applied to multitalker separation tasks, or specifically, multiple tasks from the same domain where order of the output doesn't matter much.