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].

This paper applies the idea of normalizing flows (NFs), which allows us to build complex densities with tractable likelihoods, to maximum entropy constrained optimization.  The paper is clearly written and is easy to follow.  Novelty is a weak factor in this paper. The main contributions come from (1) applying previous work on NFs to the problem of MaxEnt estimation and (2) addressing some of the optimization issues resulting from stochastic approximations to E[||T||] in combination with the annealing of Lagrange multipliers. Applying the NFs to MaxEnt is in itself not very novel as a framework. For instance, one could obtain a loss equivalent to the main loss in eq. (6) by minimizing the KLD between KL[p_{\phi};f], where f is the unormalized likelihood f \propto exp \sum_k( - \lambda_k T - c_k ||T_k||^2  ). This type of derivation is typical in all previous works using NFs for variational inference. A few experiments on more complex data would strengthen the paper's results. The two experiments provided show good results but both of them are toy problems.   Minor point:  Although intuitive, it would be good to have a short discussion of step 8 of algorithm 1 as well.
en

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” (
en

Me parece un trabajo interesante, con énfasis en cubrir brechas prácticas. Su contenido y estructura están técnicamente bien presentados. Sin embargo, por otro lado, no existe una distinción clara entre los trabajos previos y la propuesta original de este trabajo. El artículo expone que existen trabajos previos respecto a este maridaje CMMI-ÄGIL pero no indica si estas propuestas son suficientes o insuficientes para la mejora. Es decir, la motivación o justificación de este trabajo no está explícita.

es

