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].

Input: Consider Input: The paper shows promising results but it is difficult to read and follow. It presents different things closely related and it is difficult to asses the performance of each one. Diversity, sparsity, regularization term, tying weights. Anyway results are good.

Output: en


Input: Consider Input: This paper proposes an approach for multi-lingual named entity recognition using features from Wikipedia. By relying on a cross-lingual Wikifier, it identifies English Wikipedia articles for phrases in a target language and uses features based on the wikipedia entry. Experiments show that this new feature helps not only in the monolingual case, but also in the more interesting direct transfer setting, where the English model is tested on a target language.  I liked this paper. It proposes a new feature for named entity recognition and conducts a fairly thorough set of experiments to show the utility of the feature. The analysis on low resource and the non-latin languages are particularly interesting.  But what about named entities that are not on Wikipedia? In addition to the results in the paper, it would be interesting to see results on how these entities are affected by the proposed method.   The proposed method is strongly dependent on the success of the cross-lingual wikifier. With this additional step in the pipeline, how often do we get errors in the prediction because of errors in the wikifier?  Given the poor performance of direct transfer on Tamil and Bengali when lexical features are added, I wonder if it is possible to regularize the various feature classes differently, so that the model does not become over-reliant on the lexical features.

Output: en


Input: Consider Input: Sorry to pop up late, but I've been looking over ICLR accepted papers, and I noticed this one. Something very similar, by combining word and character information at the feature level using sigmoid gating, has been done before, see 


Output: en