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: - Strengths: i. Well organized and easy to understand ii. Provides detailed comparisons under various experimental settings and shows the state-of-the-art performances  - Weaknesses: i. In experiments, this paper compares previous supervised approaches, but the proposed method is the semi-supervised approach even if the training data is enough to train.  - General Discussion: This paper adopts a pre-training approach to improve Chinese word segmentation. Based on the transition-based neural word segmentation, this paper aims to pre-train incoming characters with external resources (punctuation, soft segmentation, POS, and heterogeneous training data) through multi-task learning. That is, this paper casts each external source as an auxiliary classification task. The experimental results show that the proposed method achieves the state-of-the-art performances in six out of seven datasets.   This paper is well-written and easy to understand. A number of experiments prove the effectiveness of the proposed method. However, there exist an issue in this paper. The proposed method is a semi-supervised learning that uses external resources to pre-train the characters. Furthermore, this paper uses another heterogeneous training datasets even if it uses the datasets only for pre-training. Nevertheless, the baselines in the experiments are based on supervised learning. In general, the performance of semi-supervised learning is better than that of supervised learning because semi-supervised learning makes use of plentiful auxiliary information. In the experiments, this paper should have compared the proposed method with semi-supervised approaches.  POST AUTHOR RESPONSE  What the reviewer concerned is that this paper used additional “gold-labeled” dataset to pretrain the character embeddings. Some baselines in the experiments used label information, where the labels are predicted automatically by their base models as the authors pointed out. When insisting superiority of a method, all circumstances should be same. Thus, even if the gold dataset isn’t used to train the segmentation model directly, it seems to me that it is an unfair comparison because the proposed method used another “gold” dataset to train the character embeddings.
Output: 

 en