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].

Positive Example 1 -
Input: Este artículo no es un artículo de investigación, ya que sólo muestra cómo programar un robot mediante la herramienta de lógica difusa. Este tema ya ha sido propuesto como solución en navegación de robots.
Output: es

Positive Example 2 -
Input: I don't think the work reported in this paper is suitable for the SCCC conference. The author should consider reporting this experience on a conference more focused on practice in computer engineering than in CS reserarch. The material presented in the paper could be of interest for practicioners working with Oracle, namely a particular product.
Output: en

Negative Example 1 -
Input: Este artículo no es un artículo de investigación, ya que sólo muestra cómo programar un robot mediante la herramienta de lógica difusa. Este tema ya ha sido propuesto como solución en navegación de robots.
Output: en

Negative Example 2 -
Input: I don't think the work reported in this paper is suitable for the SCCC conference. The author should consider reporting this experience on a conference more focused on practice in computer engineering than in CS reserarch. The material presented in the paper could be of interest for practicioners working with Oracle, namely a particular product.
Output: es

Now complete the following example -
Input: 26: An End-to-End Model for Question Answering over Knowledge Base with Cross-Attention Combining Global Knowledge  This paper presents an approach for factoid question answering over a knowledge graph (Freebase), by using a neural model that attempts to learn a semantic correlation/correspondence between various "aspects" of the candidate answer (e.g., answer type, relation to question entity, answer semantic, etc.) and a subset of words of the question. A separate correspondence component is learned for each "aspect" of the candidate answers. The two key contributions of this work are: (1) the creation of separate components to capture different aspects of the candidate answer, rather than relying on a single semantic representation, and (2) incorporating global context (from the KB) of the candidate answers.  The most interesting aspect of this work, in my opinion, is the separation of candidate answer representation into distinct aspects, which gives us (the neural model developer) a little more control over guiding the NN models towards information that would be more beneficial in its decision making. It sort of harkens to the more traditional algorithms that rely on feature engineering. But in this case the "feature engineering" (i.e., aspects) is more subtle, and less onerous. I encourage the authors to continue refining this system along these lines.  While the high-level idea is fairly clear to a reasonably informed reader, the devil in the details would make it hard for some audience to immediately grasp key insights from this work. Some parts of the paper could benefit from more explanation... Specifically:  (1) Context aspect of candidate answers (e_c) is not clearly explained in the paper. Therefore, the last two sentences of Section 3.2.2 seem unclear.  (2) Mention of OOV in the abstract and introduction need more explanation. As such, I think the current exposition in the paper assumes a deep understanding of prior work by the reader.  (3) The experiments conducted in this paper restrict comparison to IR-based system -- and the reasoning behind this decision is reasonable. But it is not clear then why the work of Yang et al. (2014) -- which is described to be SP-based -- is part of the comparison. While, I am all for including more systems in the comparison, there seem to be some inconsistencies in what should and should not be compared. Additionally, I see not harm in also mentioning the comparable performance numbers for the best SP-based systems.  I observe in the paper that the embeddings are learned entirely from the training data. I wonder how much impact the random initialization of these embeddings has on the end performance. It would be interesting to determine (and list) the variance if any. Additionally, if we were to start with pre-trained embeddings (e.g., from word2vec) instead of the randomly initialized ones, would that have any impact?  As I read the paper, one possible direction of future work that occurred to me was to possibly include structured queries (from SP-based methods) as part of the cross-attention mechanism. In other words, in addition to using the various aspects of the candidate answers as features, one could include structured queries that generate the produce that candidate answer as an additional aspect of the candidate answer. An attention mechanism could then also focus on various parts of the structured query, and its (semantic) matches to the input question as an additional signal for the NN model. Just a thought.  Some notes regarding the positioning of the paper:  I hesitate to call the model proposed here "attention" models, because (per my admittedly limited understanding) attention mechanisms apply to "encoder-decoder" situations, where semantics expressed in one structured form (e.g., image, sentence in one language, natural language question, etc.) are encoded into an abstract representation, and then generated into another structured form (e.g., caption, sentence in another language, structured query, etc.). The attention mechanism allows the "encoder" to jump around and attend to different parts of the input (instead of sequentially) as the output is being generated by the decoder. This paper does not appear to fit this notion, and may be confusing to a broader audience.  ------  Thank you for clarifications in the author response.
Output: 

 en