instruction:
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].
question:
Este es un trabajo que muestra la experiencia de incorporación de un robot para realizar soldaduras en una empresa de Antofagasta.  La incorporación de tecnología es importante en la Empresa y me parece importante que las empresas confíen en las Universidades para hacerlo. Es un tema importante de revisar en las jornadas.  Revisar aspectos de formato (redacción en tercera persona, ortografía, algunas imágenes no son claras, contiene 7 páginas y sólo eran consideradas 6 o 10) No queda clara si la Empresa utiliza robot y soldadores o sólo robots. No hay una evaluación de los beneficios en cuanto a dinero percibidos por la utilización del robot.
answer:
es


question:
[Summary] This paper proposes a new way for knowledge base completion which highlights: 1) adopting an implicit shared memory, which makes no assumption about its structure and is completely learned during training; 2) modeling a multi-step search process that can decide when to terminate.  The experimental results on WN18 and FB15k seem pretty good. The authors also perform an analysis on a shortest path synthetic task, and demonstrate that this model is better than standard seq2seq.  The paper is well-written and it is easy to follow.  [Major comments] I actually do like the idea and am also impressed that this model can work well. The main concern is that this paper presents too little analysis about how it works and whether it is sensitive to the hyper-parameters, besides that only reporting a final model on WN18 and FB15k.  One key hyper-parameter I believe is the size of shared memory (using 64 for the experiments). I don’t think that this number should be fixed for all tasks, at least it should depend on the KB scale. Could you verify this in your experiments? Would it be even possible to make a memory structure with dynamic size?  The RL setting (stochastic search process) is also one highlight of the paper, but could you demonstrate that how much it does really help? I think it is necessary to compare to the following: remove the termination gate and fix the number of inference steps and see how well the model does? Also show how the performance varies on # of steps?  I appreciate your attempts on the shortest path synthetic task. However, I think it would be much better if you can demonstrate that under a real KB setting. You can still perform the shortest path analysis, but using KB  (e.g., Freebase) entities and relations.  [Minor comments] I am afraid that the output gate illustrated in Figure 1 is a bit confusing. There should be only one output, depending on when the search process is terminated.
answer:
en


question:
This paper proposes a hybrid architecture that combines traditional CNN layers with separable RNN layers that quickly increase the receptive field of intermediate features. The paper demonstrates experiments on CIfar-10 and semantic segmentation, both by fine-tuning pretrained CNN models and by training them from scratch, showing numerical improvements.     The reviewers agreed that this paper presents a sound modification of standard CNN architectures in a clear, well-presented manner. They also highlighted the clear improvement of the manuscipt between the first draft and subsequent revisions.   However, they also agreed that the novelty of the approach is limited compared to recent works (e.g. Bell'16), despite acknowledging the multiple technical differences between the approaches. Another source of concern is the lack of large-scale experiments on imagenet, which would potentially elucidate the role of the proposed interleaved lrnn modules in the performance boost and demonstrate its usefulness to other tasks.     Based on these remarks, the AC recommends rejection of the current manuscript, and encourages the authors to resubmit the work once the large-scale experiments are completed.
answer:

en