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 gives a theoretical motivation for tieing the word embedding and output projection matrices in RNN LMs. The argument uses an augmented loss function which spreads the output probability mass among words with close word-embedding.   I see two main drawbacks from this framework: The augmented loss function has no trainable parameters and is used for only for regularization. This is not expected to give gains with large enough datasets.  The augmented loss is heavily “engineered” to produce the desired result of parameter tying. It’s not clear what happens if you try to relax it a bit, by adding parameters, or estimating y~ in a different way.   Nevertheless the argument is very interesting, and clearly written. The simulated results indeed validate the argument, and the PTB results seem promising.  Minor comments: Section 3: Can you clarify if y~ is conditioned on the t example or on the entire history. Eq. 3.5: i is enumerated over V (not |V|)
[A]: en


[Q]: This is a nice paper on morphological segmentation utilizing word  embeddings. The paper presents a system which uses word embeddings to  both measure local semantic similarity of word pairs with a potential  morphological relation, and global information about the semantic validity of potential morphological segment types. The paper is well written and  represents a nice extension to earlier approaches on semantically driven  morphological segmentation.  The authors present experiments on Morpho Challenge data for three  languages: English, Turkish and Finnish. These languages exhibit varying  degrees of morphological complexity. All systems are trained on Wikipedia  text.   The authors show that the proposed MORSE system delivers clear  improvements w.r.t. F1-score for English and Turkish compared to the well  known Morfessor system which was used as baseline. The system fails to  reach the performance of Morfessor for Finnish. As the authors note, this  is probably a result of the richness of Finnish morphology which leads to  data sparsity and, therefore, reduced quality of word embeddings. To  improve the performance for Finnish and other languages with a similar  degree of morphological complexity, the authors could consider word  embeddings which take into account sub-word information. For example,  @article{DBLP:journals/corr/CaoR16,   author    = {Kris Cao and                Marek Rei},   title     = {A Joint Model for Word Embedding and Word Morphology},   journal   = {CoRR},   volume    = {abs/1606.02601},   year                  = {2016},   url                 = {[Link]},   timestamp = {Fri, 01 Jul 2016 17:39:49 +0200},   biburl    = {[Link]},   bibsource = {dblp computer science bibliography, [Link]} }  @article{DBLP:journals/corr/BojanowskiGJM16,   author    = {Piotr Bojanowski and                Edouard Grave and                Armand Joulin and                Tomas Mikolov},   title     = {Enriching Word Vectors with Subword Information},   journal   = {CoRR},   volume    = {abs/1607.04606},   year                  = {2016},   url                 = {[Link]},   timestamp = {Tue, 02 Aug 2016 12:59:27 +0200},   biburl    = {[Link]},   bibsource = {dblp computer science bibliography, [Link]} }  The authors critique the existing Morpho Challenge data sets.  For example, there are many instances of incorrectly segmented words in  the material. Moreover, the authors note that, while some segmentations  in the the data set may be historically valid (for example the  segmentation of business into busi-ness), these segmentations are no  longer semantically motivated. The authors provide a new data set  consisting of 2000 semantically motivated segmentation of English word  forms from the English Wikipedia. They show that MORSE deliver highly  substantial improvements compared to Morfessor on this data set.  In conclusion, I think this is a well written paper which presents  competitive results on the interesting task of semantically driven  morphological segmentation. The authors accompany the submission with  code and a new data set which definitely add to the value of the  submission.
[A]: en


[Q]: En mi opinión, siendo la propuesta de muy alto nivel, no se puede apreciar su grado de relevancia y utilidad.  En algunos pasajes la redacción hace difícil la lectura. Esto ocurre a lo largo de todo el documento.  Presenta una revisión de los conceptos de data warehouse y data mining muy básica, que no está basada en una revisión de literatura relevante.  Carece de elementos concretos que puedan utilizar para desarrollar una solución a un problema concreto.  La bibliografía, tal cual se presenta, está fuera de toda norma utilizada en artículos científicos.  Se sugiere ya sea reenfocar el estudio hacia algo más concreto, o bien proveer evidencias que permitan evaluar de mejor forma la propuesta.
[A]:
es