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].

Example Input: This paper proposed a hardware accelerator for DNN. It utilized the fact that DNN are very tolerant to low precision inference and outperforms a state-of-the-art bit-parallel accelerator by 1.90x without any loss in accuracy while it is 1.17x more energy efficient. TRT requires no network retraining. It achieved super linear scales of performance with area. The first concern is that this paper doesn't seem very well-suited to ICLR. The circuit diagrams makes it more interesting for the hardware or circuit design community. The second concern is the "take-away for machine learning community", seeing from the response, the take-away is using low-precision to make inference cheaper. This is not novel enough. In last year's ICLR, there were at least 4 papers discussing using low precision to make DNN more efficient. These ideas have also been explored in the authors' previous papers.
Example Output: en

Example Input: Se presenta una aplicacion que consiste en un sistema de vigilancia basado en camaras ip y que puede ser monitoreado desde dispositivos moviles con android, donde el aporte del trabajo es crear un sistema centralizado para gestionar las camaras. El paper muestra en detalle el diseno del sistema, lo cual puede ser de interes a la audiencia del congreso. El trabajo presenta algunos problemas en el formato: - presenta encabezados en cada pagina que parecen referenciar a que fue extraido o enviado a una revista, lo cual no se adecua al formato del workshop -muestra tambien fechas de envio y recepcion, lo cual parece apunta en la misma direccion - presenta algunos problemas de redaccion y sentido, como: "Tambien, a esto se suma la incomodidad el tener acceso de un computador" "presenta el desarrollo de un sistema de video vigilancia con camaras IP, que engloba las desventajas?? antes nombradas" El paper deberia arreglar los problemas en el formato para ser aceptado
Example Output: es

Example Input: - Strengths: This paper proposes to apply NLP to speech transcripts (narratives and descriptions) in order to identify patients with MCI (mild cognitive impairment, ICD-10 code F06.7). The authors claim that they were able to distinguish between healthy control participants and patients with MCI (lines 141-144). However in the conclusion, lines 781-785, they say that " accuracy ranging from 60% to 85% . means that it is not easy to distinguish between healthy subjects and those with cognitive impairments". So the paper beginning is more optimistic than the conclusion but anyway the message is encouraging and the reader becomes curious to see more details about what has been actually done. The corpus submitted in the dataset is constructed for 20 healthy patients and 20 control participants only (20+20), and it is non-understandable for people who do not speak Portuguese. It would be good to incorporate more technological details in the article and probably to include at least one example of a short transcript that is translated to English, and eventually a (part of a) sample network with embeddings for this transcript. - Weaknesses: The paper starts with a detailed introduction and review of relevant work. Some of the cited references are more or less NLP background so they can be omitted e.g. (Salton 1989) in section 4.2.3. Other references are not directly related to the topic e.g. "sentiment classification" and "pedestrian detection in images", lines 652-654, and they can be omitted too. In general lines 608-621, section 4.2.3 can be shortened as well etc. etc. The suggestion is to compress the first 5 pages, focusing the review strictly on the paper topic, and consider the technological innovation in more detail, incl. samples of English translations of the ABCD and/or Cindarela narratives. The relatively short narratives in Portuguese esp. in ABCD dataset open the question how the similarities between words have been found, in order to construct word embeddings. In lines 272-289 the authors explain that they generate word-level networks from continuous word representations. What is the source for learning the continuous word representations; are these the datasets ABCD+Cinderella only, or external corpora were used? In lines 513-525 it is written that sub-word level (n-grams) networks were used to generate word embeddings. Again, what is the source for the training? Are we sure that the two kinds of networks together provide better accuracy? And what are the "out-of-vocabulary words" (line 516), from where they come? - General Discussion: It is important to study how NLP can help to discover cognitive impairments; from this perspective the paper is interesting. Another interesting aspect is that it deals with NLP for Portuguese, and it is important to explain how one computes embeddings for a language with relatively fewer resources (compared to English). The text needs revision: shortening sections 1-3, compressing 4.1 and adding more explanations about the experiments. Some clarification about the NURC/SP N. 338 EF and 331 D2 transcription norms can be given. Technical comments: Line 029: ' as it a lightweight ' -> shouldn't this be ' as in a lightweight ' Line 188: PLN -> NLP Line 264: 'out of cookie out of the cookie' some words are repeated twice Table 3, row 2, column 3: 72,0 -> 72.0 Lines 995-996: the DOI number is the same as the one at lines 1001-1002; the link behind the title at lines 992-993 points to the next paper in the list
Example Output:
en