You will be given a definition of a task first, then some input of the task.
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].

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.
Output:
en