Given the task definition and input, reply with output. 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].

The paper presents a method to learn a low-dimensional state representations from raw obervation for multi-task setting. In contrast to classic multi-task learning setting where a joint representation is usually learned by exploring the transferable information among different tasks, the method aims to identify individual task and solve them separately. To this end, the authors extend the learning with robotic priors approach by extending the loss function with additional term for task coherence, i.e., a task only changes representation between training episodes. The method has been evaluated on two tasks, multi-task slot-car racing and mobile navigation to prove its efficacy. there were several unclear issues: 1. The first question is that if the method is only appealing on the scenario like the slot-car racing, otherwise it should be benchmarked with mutli-task learning. While the author made the argument in the related work, the proposed method is orthogonal to multi-task learning they did admit both explore shared knowledge between tasks. What's the advantage and disadvantage for the proposed method for general mutiple task setting, in particular over the multi-task learning? The reply of the authors was not fully satisfactory. The argument did not support the lack of comparison to multi-task joint-learning. It seems they don't plan to include any comparison neither. I think it's important for the fundamental motivation for the work, without such comparison, the method seems to be purely an alternative to multi-task joint-learning without any(or much) practical advantage. 2.Following up to the previous question, please clarify the results on the mobile navigation scenario. It's not clear how the plot on the right indicates MT-LRP identifies all tasks as the author claimed and and seems very weak to support the method, in particular compared to the multi-slot car-racing driving experiment, there is too little results to make sound argument (almost no comparison to alternative methods, i.e. no baseline method, is that true for the problem). The explanation of the authors did provide more details and more explicit information. 3. The proposed gated neural network architecture seems to be a soft gated structure(correct me if I am wrong), a possible baseline would be a hard gated unit, how would this affect the conclusion. This is particularly interesting as the authors reflect on the constraint that the representation should stay consistent during the training. The author simply stated again what they did for the modeling without counter the comparison to hard-gating, but it's probably less an issue compared to Question 1. In summary, while there are remaining concerns about lacking comparisons, the is a weak tendency towards accepting the submission.
en