From: NIPS.cc/2018/Workshop/IRASL
title: Acceptance Decision
decision: Accept

From: NIPS.cc/2018/Workshop/IRASL/Paper57/AnonReviewer1
title: This paper explores if HMMs are a special case of RNNs using two tasks: language modeling and POS tagging. The authors  transform the HMM equations to an RNN and viceversa and empericaly study the effects of the independence assumption, softmax and non-linearity  This is relevant to the theme of the workshop.
review: The interpretation of RNNs via HMMs is not novel, however the authors do a structured approach to understand the specific effects of independence assumption, non-linearity and softmax.
The language modeling results presented are on the Penn Tree Bank corpus.  The reported perplexity numbers (possibly because of the one-layer net) are nowhere near state-of-the -art for this task.  This makes the conclusions hard to evaluate. Will the interpretations change if the  models are significantly better?

The work is still useful to present at a workshop.

It is surprising to see that use of the previous hidden state and input embedding, is less useful.

The paper is a bit hard to read and could use better notations.  Fig. 1 is impossible to read.



rating: 4: Top 50% of accepted papers, clear accept
confidence: 3: The reviewer is absolutely certain that the evaluation is correct and very familiar with the relevant literature

From: NIPS.cc/2018/Workshop/IRASL/Paper57/AnonReviewer3
title: Discrete HMMs and RNNs, design variations
review: The authors present a structured approach to compare and analyse HMMs and RNNs for discrete label sequence modeling. They rewrite HMMs into RNN-like notation, and transform HMMs towards RNNs and vice-versa, followed by a comparative analysis for language modeling and POS tagging. The chosen notation sometimes is hard to follow.

The experimental results are hard to evaluate, as e.g. language model perplexities are far below state-of-the-art. The authors provide an LSTM result for comparison with PP around 80. However, current language modeling results on PTB go down to PPLs around the 50s, so the relevance of the results shown here are not clear to me.

Eq. 9: on the right hand side, you possibly mean w^(1:i-1). Using x and bold x with quite different meaning does not help readability. 

Table 1 & 2: there are unspecified results/lines with only a hyphen - if they refer to no modification, term it "unmodified" or similar.

Detailed comments:

 - Fig. 1 definitely needs to be redone - the fonts used within the figure are unreadable (unless zooming to poster size...).    
   Also, the background colours used in the flowcharts should be somewhat lighter to increase contrast to the embedded 
   text.

-Fig. 2: Again, fonts within the figure a way too small.

rating: 3: Marginally above acceptance threshold
confidence: 2: The reviewer is fairly confident that the evaluation is correct

From: NIPS.cc/2018/Workshop/IRASL/Paper57/AnonReviewer2
title: A very nice paper linking HMMs and RNNs
review: This paper establishes a clear connection between RNNs and HMMs: 
The forward pass (the filtering density) of an RNN can basically be thought as an RNN with a particular structure. 

Based on this observation, the paper explores different but close design choices (such as changing the non-linearities) 

These 'close' architectures, along with standard RNNs and HMM is trained with gradient descent and performance is compared on two tasks. 

Although the proposed RNN like HMMs are not as good as a vanilla LSTM in language modeling, the HMM models are more interpretable, and therefore there is merit to exploring more 'HMM like' RNN architectures (in the POS tagging experiments this manifests itself). 

This is a very good paper, highly informative for readers. A clear accept. 
rating: 4: Top 50% of accepted papers, clear accept
confidence: 2: The reviewer is fairly confident that the evaluation is correct