text: The paper presents a way to regularize a sequence generator by making the hidden states also predict the hidden states of an RNN working backward. Applied to sequence-to-sequence networks, the approach requires training one encoder, and two separate decoders, that generate the target sequence in forward and reversed orders.  A penalty term is added that forces an agreement between the hidden states of the two decoders.  During model evaluation only the forward decoder is used, with the backward operating decoder discarded.  The method can be interpreted to generalize other recurrent network regularizers, such as putting an L2 loss on the hidden states. Experiments indicate that the  approach is most successful when the regularized RNNs are conditional generators, which emit sequences of low entropy, such as decoders of a seq2seq speech recognition network.  Negative results were reported when the proposed regularization technique was applied to language models, whose output distribution has more entropy. The proposed regularization is evaluated with positive results on a speech recognition task and on an  image captioning task, and with negative results (no improvement, but also no deterioration) on a language modeling and sequential MNIST digit generation tasks. I have one question about baselines: is the proposed approach better than training to forward generators and force an agreement between them (in the spirit of the concurrent ICLR submission https://openreview.net/forum?id=rkr1UDeC-)?  Also, would using the backward RNN, e.g. for rescoring, bring another advantage?  In other words, what is (and is there) a gap between an ensemble of a forward and backward rnn and the forward-rnn only, but trained with the state-matching penalty? Quality:The proposed approach is well motivated  and the experiments show the limits of applicability range of the technique. Clarity:The paper is clearly written. Originality:The presented idea seems novel. Significance:The method may prove to be useful to regularize recurrent networks,  however I would like to see a comparison with ensemble methods.  Also, as the authors note the method seems to be limited to conditional sequence generators. Pros and cons:Pros: the method is simple to implement,  the paper lists for what kind of datasets it can be used. Cons: the method needs to be compared with typical ensembles of models going only forward in time,  it may turn that it using the backward RNN is not necessary