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].

Q: This paper proposes a neural architecture for answering non-factoid questions. The author's model improves over previous neural models for answer sentence selection. Experiments are conducted on a Japanese love advice corpus; the coolest part of the paper for me was that the model was actually rolled out to the public and its answers were rated twice as good as actual human contributors!   It was hard for me to determine the novelty of the contribution. The authors mention that their model "fills the gap between answer selection and generation"; however, no generation is actually performed by the model! Instead, the model appears to be very similar to the QA-LSTM of Tan et al., 2015 except that there are additional terms in the objective to handle conclusion and supplementary sentences. The structure of the answer is fixed to a predefined template (e.g., conclusion --> supplementary), so the model is not really learning how to order the sentences. The other contribution is the "word embedding with semantics" portion described in sec 4.1, which is essentially just the paragraph vector model except with "titles" and "categories" instead of paragraphs.   While the result of the paper is a model that has actually demonstrated real-life usefulness, the technical contributions do not strike me as novel enough for publication at ICLR.  Other comments: - One major issue with the reliance of the model on the template is that you can't evaluate on commonly-used non-factoid QA datasets such as InsuranceQA. If the template were not fixed beforehand (but possibly learned by the model), you could conceivably evaluate on different datasets.  - The examples in Table 4 don't show a clear edge in answer quality to your model; QA-LSTM seems to choose good answers as well. - Doesn't the construction model have an advantage over the vanilla QA-LSTM in that it knows which sentences are conclusions and which are supplementary? Or does QA-LSTM also get this distinction?

A: en
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Q: While I understand the difficulty of collecting audio data from animals, I think this type of feature engineering does not go in the right direction. I would rather see a model than learns the feature representation from data.  I would think it should be possible to collect a more substantial corpus in zoos / nature etc, and then train a generative model. The underlying learned feature representation could be then used to feed a classifier. I'm not familiar with the particularities of this task, it's hard to judge the improvements by using chirplets.

A: en
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Q: el trabajo presenta una posible solución a una problemática real, es interesante la propuesta de la SOS veterinaria. En el trabajo se plantean elementos importantes a tener en cuenta para el desarrollo de una aplicación móvil, aunque en algunas partes del texto es presentada como un desarrollo web. En el trabajo promete el desarrollo de la aplicación. El articulo muestra cómo podría obtenerse una mayor rentabilidad y una mejor gestión organizativa en un SOS Veterinaria, pero no hay un aporte claro que se evidencie en la implementación de un sistema de este tipo.  La figura 2 Process Diagram Level 0 of the Application debe mejorarse, es borrosa. En la figura 3 se presenta la descripción de la solución pero la información debería acompañarse de información que dé cuenta del proceso de forma detallada. hace falta identificar la manera de almacenar y gestionar los datos. La información estará almacenada en una BD? en archivos XML?..... hace falta una pequeña descripción de la arquitectura del sistema. La calidad de las figuras 4 hasta la 12 no es buena, los textos son borrosos.  El artículo debería centrarse en el aporte y los resultados de la aplicación de este tipo de desarrollos.  Se mencionan muchas tecnologías y técnicas de desarrollo pero no es evidente en el desarrollo de la SOS veterinaria.

A:
es
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