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].
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Question: El artículo presenta una aplicación de técnicas de reconocimiento de patrones a la clasificación de tipos de maderas basada en registros de audio.  En los aspectos de forma:  El artículo presenta varios problemas de sintaxis del lenguaje español, los cuales son fácilmente subsanables. El resumen se debería revisar y mejorar.  También se presentan algunos términos en inglés que deberían ser reemplazados por su significado en español (e.g. test).  Las ecuaciones 4 y 5 contienen errores y deben ser mejor explicadas. También ajustar el tamaño de la letra (font size).  La tabla 1 contiene información redundante, pues las cantidades que se presentan parecen ser independientes del método.  Mejorar la resolución y aumentar el tamaño de la figura 5 (que ocupe las 2 columnas), pues no se logra apreciar bien los distintos puntos y clusters que se comentan.  En los aspectos de fondo:  La revisión del estado del arte es débil, debiendo incorporarse más referencias actuales relevantes  relacionadas al reconocimiento de patrones en base a datos de audio (aunque no necesariamente sean en el dominio de las maderas).  La versión del algoritmo KNN cuyo pseudocódigo se muestra, esta incorrecto. Se debe revisar y corregir. No se aprecia que trabaje para un K determinado. Tampoco se aprecia la recursión.  Se requiere mayor información y detalle de los datos y de las características derivadas.

Answer: es


Question: Thank you for an interesting read.  I found this paper very interesting. Since I don't think (deterministic) approximate inference is separated from the modelling procedure (cf. exact inference), it is important to allow the users to select the inference method to suit their needs and constraints. I'm not an expert of PPL, but to my knowledge this is the first package that I've seen which put more focus on compositional inference. Leveraging tensorflow is also a plus, which allows flexible computation graph design as well as parallel computation using GPUs.  The only question I have is about the design of flexible objective functions to learn hyper-parameters (or in the paper those variables associated with delta q distributions). It seems hyper-parameter learning is also specified as inference, which makes sense if using MAP. However the authors also demonstrated other objective functions such as Renyi divergences, does that mean the user need to define a new class of inference method whenever they want to test an alternative loss function?

Answer: en


Question: The paper introduces a general method for improving NLP tasks using embeddings from language models. Context independent word representations have been very useful, and this paper proposes a nice extension by using context-dependent word representations obtained from the hidden states of neural language models. They show significant improvements in tagging and chunking tasks from including embeddings from large language models. There is also interesting analysis which answers several natural questions.  Overall this is a very good paper, but I have several suggestions: - Too many experiments are carried out on the test set. Please change Tables 5 and 6 to use development data - It would be really nice to see results on some more tasks - NER tagging and chunking don't have many interesting long range dependencies, and the language model might really help in those cases. I'd love to see results on SRL or CCG supertagging. - The paper claims that using a task specific RNN is necessary because a CRF on top of language model embeddings performs poorly. It wasn't clear to me if they were backpropagating into the language model in this experiment - but if not, it certainly seems like there is potential for that to make a task specific RNN unnecessary.

Answer:
en