Reviews: Reverse engineering recurrent networks for sentiment classification reveals line attractor dynamics

UPDATE after reading author rebuttal: Look forward to the changes in the final version of the paper. Detailed comments: 1. Understanding of RNNs for sentiment classification task - theoretical analysis backed by empirical observations: This work takes up the sentiment classification task. This work figured out some fixed points and centered their analysis of RNNs around them. The RNN states can be cast into a 1-dimensional manifold of these fixed points. The PCA of RNN states across examples reveal that training helps RNNs figure out a lower-dimensional representation. Interestingly the movement along this low dimensional manifold is minimal in absence of inputs or presence of neutral/un-informative words, whereas they show more movements if polarity bearing words are present, thus, showing linear separability effects along this 1-D manifold.