Task: Language Identification
Input (Spanish, English): This paper proposes SEM, a simple large-size multilabel learning algorithm which models the probability of each label as softmax(sigmoid(W^T X) + b), so a one-layer hidden network. This in and of itself is not novel, nor is the idea of optimizing this by adagrad. Though it's weird that the paper explicitly derives the gradient and suggests doing alternating adagrad steps instead of the more standard adagrad steps; it's unclear whether this matters at all for performance. The main trick responsible for increasing the efficiency of this model is the candidate label sampling, which is done in a relatively standard way by sampling labels proportionally to their frequency in the dataset.  Given that neither the model nor the training strategy is novel, it's surprising that the results are better than the state-of-the-art in quality and efficiency (though non-asymptotic efficiency claims are always questionable since implementation effort trades off fairly well against performance). I feel like this paper doesn't quite meet the bar.
Output (English): en