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 gives a theoretical motivation for tieing the word embedding and output projection matrices in RNN LMs. The argument uses an augmented loss function which spreads the output probability mass among words with close word-embedding.   I see two main drawbacks from this framework: The augmented loss function has no trainable parameters and is used for only for regularization. This is not expected to give gains with large enough datasets.  The augmented loss is heavily “engineered” to produce the desired result of parameter tying. It’s not clear what happens if you try to relax it a bit, by adding parameters, or estimating y~ in a different way.   Nevertheless the argument is very interesting, and clearly written. The simulated results indeed validate the argument, and the PTB results seem promising.  Minor comments: Section 3: Can you clarify if y~ is conditioned on the t example or on the entire history. Eq. 3.5: i is enumerated over V (not |V|)
en

This paper describes a new deterministic dependency parsing algorithm and analyses its behaviour across a range of languages. The core of the algorithm is a set of rules defining permitted dependencies based on POS tags. The algorithm starts by ranking words using a slightly biased PageRank over a graph with edges defined by the permitted dependencies. Stepping through the ranking, each word is linked to the closest word that will maintain a tree and is permitted by the head rules and a directionality constraint.  Overall, the paper is interesting and clearly presented, though seems to differ only slightly from Sogaard (2012), "Unsupervised Dependency Parsing without Training". I have a few questions and suggestions:  Head Rules (Table 1) - It would be good to have some analysis of these rules in relation to the corpus. For example, in section 3.1 the fact that they do not always lead to a connected graph is mentioned, but not how frequently it occurs, or how large the components typically are.  I was surprised that head direction was chosen using the test data rather than training or development data. Given how fast the decision converges (10-15 sentences), this is not a major issue, but a surprising choice.  How does tie-breaking for words with the same PageRank score work? Does it impact performance significantly, or are ties rare enough that it doesn't have an impact?  The various types of constraints (head rules, directionality, distance) will lead to upper bounds on possible performance of the system. It would be informative to include oracle results for each constraint, to show how much they hurt the maximum possible score. That would be particularly helpful for guiding future work in terms of where to try to modify this system.  Minor:  - 4.1, "we obtain [the] rank"  - Table 5 and Table 7 have columns in different orders. I found the Table 7 arrangement clearer.  - 6.1, "isolate the [contribution] of both"
en

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