Title: Bridging HMMs and RNNs through Architectural Transformations. Abstract: A distinct commonality between HMMs and RNNs is that they both learn hidden representations for sequential data. In addition, it has been noted that the backward computation  of  the  Baum-Welch  algorithm  for  HMMs  is  a  special  case  of  the back propagation algorithm used for neural networks (Eisner (2016)).  Do these observations  suggest  that,  despite  their  many apparent  differences,  HMMs  are a special case of RNNs?   In this paper,  we investigate a series of architectural transformations between HMMs and RNNs, both through theoretical derivations and empirical hybridization, to answer this question. In particular, we investigate three key design factors—independence assumptions between the hidden states and the observation, the placement of softmax, and the use of non-linearity—in order to pin down their empirical effects.  We present a comprehensive empirical study to provide insights on the interplay between expressivity and interpretability with respect to language modeling and parts-of-speech induction. 