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].

Ex Input:
This paper proposes a neural architecture for answering non-factoid questions. The author's model improves over previous neural models for answer sentence selection. Experiments are conducted on a Japanese love advice corpus; the coolest part of the paper for me was that the model was actually rolled out to the public and its answers were rated twice as good as actual human contributors! It was hard for me to determine the novelty of the contribution. The authors mention that their model "fills the gap between answer selection and generation"; however, no generation is actually performed by the model! Instead, the model appears to be very similar to the QA-LSTM of Tan et al., 2015 except that there are additional terms in the objective to handle conclusion and supplementary sentences. The structure of the answer is fixed to a predefined template (e.g., conclusion --> supplementary), so the model is not really learning how to order the sentences. The other contribution is the "word embedding with semantics" portion described in sec 4.1, which is essentially just the paragraph vector model except with "titles" and "categories" instead of paragraphs. While the result of the paper is a model that has actually demonstrated real-life usefulness, the technical contributions do not strike me as novel enough for publication at ICLR. Other comments: - One major issue with the reliance of the model on the template is that you can't evaluate on commonly-used non-factoid QA datasets such as InsuranceQA. If the template were not fixed beforehand (but possibly learned by the model), you could conceivably evaluate on different datasets. - The examples in Table 4 don't show a clear edge in answer quality to your model; QA-LSTM seems to choose good answers as well. - Doesn't the construction model have an advantage over the vanilla QA-LSTM in that it knows which sentences are conclusions and which are supplementary? Or does QA-LSTM also get this distinction?

Ex Output:
en

Ex Input:
The starting point of this work is the understanding that by having decorrelated neurons (e.g. neurons that only fire on background, or only on foreground regions) one provides independent pieces of information to the subsequent decisions. As such one gives "complementary viewpoints" of the input to the subsequent layers, which can be thought of as performing ensembling/expert combination within the model, rather than using an ensemble of networks. For this, the authors propose a sensible method to decorrelate the activations of intermediate neurons, with the aim of delivering complementary inputs to the final classification layers: they split intermediate neurons to a "foreground" and a "background" subset, and append side-losses that force them to be zero on background and foreground pixels respectively. They demonstrate that this can improve classification on a mid-scale classification example (a fraction of imagenet, and a ResNet with 18, rather than 150 layers), when compared to a "vanilla" baseline that does not use these losses. I enjoyed reading the paper because the idea is simple, smart, and seems to be effective. But there are a few concerns; -firstly, the way of doing this seems very particular to vision. In vision one knows that masking the features (during both training and testing) helps, e.g.

Ex Output:
en

Ex Input:
This paper discusses Hyperband, an extension of successive halving by Jamieson & Talwalkar (AISTATS 2016). Successive halving is a very nice algorithm that starts evaluating many configurations and repeatedly cuts off the current worst half to explore many configuration for a limited budget. Having read the paper for the question period and just rereading it again, I am now not entirely sure what its contribution is meant to be: the only improvement of Hyperband vs. successive halving is in the theoretical worst case bounds (not more than 5x worse than random search), but you can (a) trivially obtain that bound by using a fifth of your time for running random configurations to completion and (b) the theoretical analysis to show this is said to be beyond the scope of the paper. That makes me wonder whether the theoretical results are the contribution of this paper, or whether they are the subject of a different paper and the current paper is mostly an empirical study of the method? I hope to get a response by the authors and see this made clearer in an updated version of the paper. In terms of experiments, the paper fails to show a case where Hyperband actually performs better than the authors' previous algorithm successive halving with its most agressive setting of bracket b=4. Literally, in every figure, bracket b=4 is at least as good (and sometimes substantially better) than Hyperband. That makes me think that in practice I would prefer successive halving with b=4 over Hyperband. (And if I really want Hyperband's guarantee of not being more than 5x worse than random search I can run random search on a fifth of my machines.) The experiments also compare to some Bayesian optimization methods, but not to the most relevant very closely related Multi-Task Bayesian Optimization methods that have been dominating effective methods for deep learning in that area in the last 3 years: "Multi-Task Bayesian Optimization" by Swersky, Snoek, and Adams (2013) already showed 5x speedups for deep learning by starting with smaller datasets, and there have been several follow-up papers showing even larger speedups. Given that this prominent work on multitask Bayesian optimization exists, I also think the introduction, which sells Hyperband as a very new approach to hyperparameter optimization is misleading. I would've much preferred a more down-to-earth pitch that says "configuration evaluation" has been becoming a very important feature in hyperparameter optimization, including Bayesian optimization, that sometimes yields very large speedups (this can be quantified by examples from existing papers) and this paper adds some much-needed theoretical understanding to this and demonstrates how important configuration evaluation is even in the simplest case of being used with random search. I think this could be done easily and locally by adding a paragraph to the intro. As another point regarding novelty, I think the authors should make clear that approaches for adaptively deciding how many resources to use for which evaluation have been studied for (at least) 23 years in the ML community -- see Maron & Moore, NIPS 1993: "Hoeffding Races: Accelerating Model Selection Search for Classification and Function Approximation" (

Ex Output:
en