In this task, you are given a paper review. Based on the review, your job is to identify language and generate "en" if the review is in English or generate "es" if the review is in Spanish. Note that URLs in the text have been replaced with [Link].

[EX Q]: The authors agree with the reviewers that this manuscript is not yet ready.
[EX A]: en

[EX Q]: This paper develops Submodular Sum Product Networks (SSPNs) and an efficient inference algorithm for approximately computing the most probable labeling of variables in the model. The main application in the paper is on scene parsing. In this context, SSPNs define an energy function with a grammar component for representing a hierarchy of labels and an MRF for encoding smoothness of labels over space. To perform inference, the authors develop a move-making algorithm, somewhat in the spirit of fusion moves (Lempitsky et al., 2010) that repeatedly improves a solution by considering a large neighborhood of alternative segmentations and solving an optimization problem to choose the best neighbor. Empirical results show that the proposed algorithm achieves better energy that belief propagation of alpha expansion and is much faster.  This is generally a well-executed paper. The model is interesting and clearly defined, the algorithm is well presented with proper analysis of the relevant runtimes and guarantees on the behavior. Overall, the algorithm seems effective at minimizing the energy of SSPN models.  Having said that, I don't think this paper is a great fit for ICLR. The model is even somewhat to the antithesis of the idea of learning representations, in that a highly structured form of energy function is asserted by the human modeller, and then inference is performed. I don't see the connection to learning representations. One additional issue is that while the proposed algorithm is faster than alternatives, the times are still on the order of 1-287 seconds per image, which means that the applicability of this method (as is) to something like training ConvNets is limited.  Finally, there is no attempt to argue that the model produces better segmentations than alternative models. The only evaluations in the paper are on energy values achieved and on training data.  So overall I think this is a good paper that should be published at a good machine learning conference, but I don't think ICLR is the right fit.
[EX A]: en

[EX Q]: - Strengths: i. Well organized and easy to understand ii. Provides detailed comparisons under various experimental settings and shows the state-of-the-art performances  - Weaknesses: i. In experiments, this paper compares previous supervised approaches, but the proposed method is the semi-supervised approach even if the training data is enough to train.  - General Discussion: This paper adopts a pre-training approach to improve Chinese word segmentation. Based on the transition-based neural word segmentation, this paper aims to pre-train incoming characters with external resources (punctuation, soft segmentation, POS, and heterogeneous training data) through multi-task learning. That is, this paper casts each external source as an auxiliary classification task. The experimental results show that the proposed method achieves the state-of-the-art performances in six out of seven datasets.   This paper is well-written and easy to understand. A number of experiments prove the effectiveness of the proposed method. However, there exist an issue in this paper. The proposed method is a semi-supervised learning that uses external resources to pre-train the characters. Furthermore, this paper uses another heterogeneous training datasets even if it uses the datasets only for pre-training. Nevertheless, the baselines in the experiments are based on supervised learning. In general, the performance of semi-supervised learning is better than that of supervised learning because semi-supervised learning makes use of plentiful auxiliary information. In the experiments, this paper should have compared the proposed method with semi-supervised approaches.  POST AUTHOR RESPONSE  What the reviewer concerned is that this paper used additional “gold-labeled” dataset to pretrain the character embeddings. Some baselines in the experiments used label information, where the labels are predicted automatically by their base models as the authors pointed out. When insisting superiority of a method, all circumstances should be same. Thus, even if the gold dataset isn’t used to train the segmentation model directly, it seems to me that it is an unfair comparison because the proposed method used another “gold” dataset to train the character embeddings.
[EX A]:
en