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 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?
Example Output: en

Example Input: El trabajo aborda el problema de la navegación autónoma de robots móviles mediante la definición de una arquitectura de control reactiva.  Los autores deben ser más claros en indicar por qué utilizar una arquitectura reactiva, en relación con otros enfoques disponibles.  Es necesario mejorar la legibilidad de las ecuaciones.  El trabajo es correcto y pertinente. El ámbito de la robótica móvil tiene múltiples aplicaciones.  Es necesario incorporar algunos lineamientos sobre trabajo futuro.
Example Output: es

Example Input: This submission proposes to learn the word decomposition, or word to sub-word sequence mapping jointly with the attention based sequence-to-sequence model. A particular feature of this approach is that the decomposition is not static, instead, it also conditions on the acoustic input, and the mapping is probabilistic, i.e., one word may map to multiple sub-word sequences. The authors argue that the dynamic decomposition approach can more naturally reflect the acoustic pattern. Interestingly, the motivation behind this approach is analogous to learning the pronunciation mixture model for HMM based speech recognition, where the probabilistic mapping from a word to its pronunciations also conditions on the acoustic input, e.g.,  I. McGraw, I. Badr, and J. Glass, "Learning lexicons form speech using a pronunciation mixture model," in IEEE Transactions on Audio, Speech, and Language Processing, 2013  L. Lu, A. Ghoshal, S. Renals, "Acoustic data-driven pronunciation lexicon for large vocabulary speech recognition", in Proc. ASRU   R. Singh, B. Raj, and R. Stern, "Automatic generation of subword units for speech recognition systems,"  in IEEE Transactions on Speech and Audio Processing, 2002  It would be interesting to put this work in the context by linking it to some previous works in the HMM framework.  Overall, the paper is well written, and it is theoretically convincing. The experimental study could be more solid, e.g., it is reasonable to have a word-level baseline, as the proposed approach lies in between the character-level and word-level systems. the vocabulary size of the WSJ si284 dataset is 20K at maximum, which is not very large for the softmax layer, and it is a closed vocabulary task. I guess the word-level system may be also competitive to the numbers reported in this paper. Furthermore, can you explain what is the computational bottleneck of the proposed approach? You downsampled the data by the factor of 4 using an RNN, and it still took around 5 days to converge. To me, it is a bit expensive, especially given that you only take one sample when computing the gradient. Table 2 is a little bit misleading, as CTC with language model and seq2seq with a language model model from Bahdanau et al. is much closer to the best number reported in this Table 2, while you may only get a very small improvement using a language model. Finally, "O(5) days to converge" sounds a bit odd to me.
Example Output:
en