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:
This paper shows that a deep RL approach augmented with auxiliary tasks improves performance on navigation in complex environments. Specifically, A3C is used for the RL problem, and the agent is simultaneously trained on an unsupervised depth prediction task and a self-supervised loop closure classification task. While the use of auxiliary tasks to improve training of models including RL agents is not new, the main contribution here is the use of tasks that encourage learning an intrinsic representation of space and movement that enables significant improvements on maze navigation tasks.  The paper is well written, experiments are convincing, and the value of the auxiliary tasks for the problem are clear. However, the contribution is relatively incremental given previous work on RL for navigation and on auxiliary tasks. The work could become of greater interest provided broader analysis and insights on either optimal combinations of tasks for visual navigation (e.g. the value of other visual / geometry-based tasks), or on auxiliary tasks with RL in general.  As it is, it is a useful demonstration of the benefit of geometry-based auxiliary tasks for navigation, but of relatively narrow interest.
answer:
en


question:
This paper aims to mine explicit rules from KB embedding space, and casts it into a sparse reconstruction problem. Experiments demonstrate its ability of extracting reasonable rules on a few link prediction datasets.  The solution part sounds plausible. However, it confuses me that why we need to mine rules from learned KB embeddings.   - It is still unclear what information these KB embeddings encode and it looks strange that we aim to learn rules including negation / disjunction from them.  - If the goal is to extract useful rules (for other applications), it is necessary to compare it to “graph random walk” (
answer:
en


question:
This paper proposes an "interactive" version of the bAbI dataset by adding supporting questions/answers to the dataset in cases where there is not enough information to answer the question. Interactive QA is certainly an interesting problem and is well-motivated by the paper. However, I don't feel like the bAbI extension is adequately explained. For example, the baseline DMN and MemN2N models on the IQA task are "take both statements and question as input and then estimate an answer." Their task is then fundamentally more difficult from the CAN's because they do not distinguish "feedback" from the original context; perhaps a more fair approach would be to treat **every** question (both supporting and original questions) as individual instances. Also, how were the supporting questions and the user feedback generated? How many templates / words were used to create them? The dataset creation details are missing, and if space is an issue, a lot of basic exposition on things like GRU / sentence encodings can be cut (or at least greatly shortened) and replaced with pointers to the original papers.   Another issue I had is that the model attempts to generate these synthetic questions; if there are just one or two templates, why not just predict the values that fill these templates? So instead of generating "Which bedroom, master one or guest one?" with an RNN decoder, just predict "which" or "which bedroom"... isn't this sufficient? In the end, these just seem like more supporting facts, not actual interaction with users, and the fact that it is run on only three of the original twenty tasks make the conclusions hard to trust.  In conclusion, I think the paper has a strong idea and motivation, but the experiments are not convincing for the paper to be accepted at ICLR.
answer:

en