TASK DEFINITION: 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].
PROBLEM: The authors describe a dataset of proof steps in higher order logic derived from a set of proven theorems. The success of methods like AlphaGo suggests that for hard combinatorial style problems, having a curated set of expert data (in this case the sequence of subproofs) is a good launching point for possibly super-human performance. Super-human ATPs are clearly extremely valuable. Although relatively smaller than the original Go datasets, this dataset seems to be a great first step. Unfortunately, the ATP and HOL aspect of this work is not my area of expertise. I can't comment on the quality of this aspect.  It would be great to see future work scale up the baselines and integrate the networks into state of the art ATPs. The capacity of deep learning methods to scale and take advantage of larger datasets means there's a possibility of an iterative approach to improving ATPs: as the ATPs get stronger they may generate more data in the form of new theorems. This may be a long way off, but the possibility is exciting.

SOLUTION: en

PROBLEM: As you noted for Figure 5 Left, sometimes it seems sufficient to tune learning rates. I see your argument for Figure 6 Right,  but  1) not for all good learning rates make Adam fail, I guess you selected the one where it did (note that Adam was several times faster than Eve in the beginning) 2) I don't buy "Eve always converges" because you show it only for 0.1 and since Eve is not Adam, 0.1 of Adam is not 0.1 of Eve because of d_t.   To my understanding, you define d_t over time with 3 hyperparameters. Similarly, one can define d_t directly. The behaviour of d_t that you show is not extraordinary and can be parameterized. If Eve is better than Adam, then looking at d_t we can directly see whether we underestimated or overestimated learning rates. You could argue that Eve does it automatically but you do tune learning rates for each problem individually anyway.

SOLUTION: en

PROBLEM: This work is an extension of previous works on pointer models, that mixes its outputs with standard softmax outputs.  The idea is appealing in general for context biasing and the specific approach appears quite simple.  The idea is novel to some extent, as previous paper had already tried to combine pointer-based and standard models, but not as a mixture model, as in this paper.  The paper is clearly written and the results seem promising. The new dataset the authors created (WikiText) also seems of high interest.   A comment regarding notation: The symbol p_ptr is used in two different ways in eq. 3 and eq. 5. : p_ptr(w) vs. p_ptr(y_i|x_i)  This is confusing as these are two different domains: for eq 3. the domain is a *set* of words and for eq. 5 the domain is a *list* of context words. It would be helpful to use different symbol for the two objects.

SOLUTION:
en