You will be given a definition of a task first, then some input of the task.
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].

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.
Output:
en