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].
--------
Question: This work combines a LDA-type topic model with a RNN and models this by having an additive effect on the predictive distribution via the topic parameters. A variational auto-encoder is used to infer the topic distribution for a given piece of text and the RNN is trained as a RNNLM. The last hidden state of the RNNLM and the topic parameters are then concatenated to use as a feature representation.  The paper is well written and easy to understand. Using the topic as an additive effect on the vocabulary allows for easy inference but intuitively I would expect the topic to affect the dynamics too, e.g. the state of the RNN. The results on using this model as a feature extractor for IMDB are quite strong. Is the RNN fine-tuned on the labelled IMDB data? However, the results for PTB are weaker. From the original paper, an ensemble of 2 LSTMs is able to match the topicRNN score. This method of jointly modelling topics and a language model seems effective and relatively easy to implement.  Finally, the IMDB result is no longer state of the art since this result appeared in May (Miyato et al., Adversarial Training Methods for Semi-Supervised Text Classification).  Some questions: How important is the stop word modelling? What do the results look like if l_t = 0.5 for all t?  It seems surprising that the RNN was more effective than the LSTM. Was gradient clipping tried in the topicLSTM case? Do GRUs also fail to work?  It is also unfortunate that the model requires a stop-word list. Is the link in footnote 4 the one that is used in the experiments?  Does factoring out the topics in this way allow the RNN to scale better with more neurons? How reasonable does the topic distribution look for individual documents? How peaked do they tend to be? Can you show some examples of the inferred distribution? The topics look odd for IMDB with the top word of two of the topics being the same: 'campbell'. It would be interesting to compare these topics with those inferred by LDA on the same datasets.  Minor comments: Below figure 2: GHz -> GB \Gamma is not defined.

Answer: en


Question: El artículo habla (en el resumen y un poco en la introducción) de una experiencia tecnológica de inter-operatividad entre plataformas de enseñanza, pero no se evidencia tal experiencia en sus líneas. Los resultados no son concretos, no hay experimentación o análisis de resultados que sustenten lo afirmado en el apartado de conclusiones. El apartado de conclusiones no se relaciona con lo dicho en el resumen no expresado en la introducción como objetivo del trabajo. A pesar de ser un estudio interesante respecto a estándares y tecnologías como e-learning o t-learning, los autores no logran describir la metodología de trabajo, procesos, experimentación, resultados y valoración de los resultados. En el apartado Resultados (al final) hablan de pruebas en dispositivos pero no se describen dichas pruebas, no hay evidencia de dichas pruebas en el documento.  Por otro lado, el texto tiene carencias importantes como las siguientes: 1. Tanto el Resumen como el Abstract tienen ideas difíciles de entender o están poco elaborados. 2. Se detectan errores ortográficos en el documento. 3. Lo dicho en la Introducción coincide poco con lo dicho en el Resumen. 4. Uso de siglas (abreviaturas) no descritas en el documento (como por ejemplo ETSI), no hay un glosario de términos. 5. Párrafos que contribuyen muy poco a entender el documento, como por ejemplo el que dice "La Tabla 1 muestra el análisis de las características de las normas y estándares" o el párrafo que dice "Pruebas de acceso y despliegue sobre el BlackBerry Thorch".

Answer: es


Question: This paper considers the problem of decoding diverge solutions from neural sequence models. It basically adds an additional term to the log-likelihood of standard neural sequence models, and this additional term will encourage the solutions to be diverse. In addition to solve the inference, this paper uses a modified beam search.  On the plus side, there is not much work on producing diverse solutions in RNN/LSTM models. This paper represents one of the few works on this topic. And this paper is well-written and easy to follow.  The novel of this paper is relatively small. There has been a lot of prior work on producing diverse models in the area of probailistic graphical models. Most of them introduce an additional term in the objective function to encourage diversity. From that perspective, the solution proposed in this paper is not that different from previous work. Of course, one can argue that most previous work focues on probabilistic graphical models, while this paper focuses on RNN/LSTM models. But since RNN/LSTM can be simply interpreted as a probabilistic model, I would consider it a small novelty.  The diverse beam search seems to straightforward, i.e. it partitions the beam search space into groups, and does not consider the diversity within group (in order to reduce the search space). To me, this seems to be a simple trick. Note most previous work on diverse solutions in probabilistic graphical models usually involve developing some nontrivial algorithmic solutions, e.g. in order to achieve efficiency. In comparison, the proposed solution in this paper seems to be simplistic for a paper.  The experimental results how improvement over previous methods (Li & Jurafsky, 2015, 2016). But it is hard to say how rigorous the comparisons are, since they are based on the authors' own implementation of (Li & Jurasky, 2015, 2016).  --------------- update: given that the authors made the code available (I do hope the code will remain publicly available), this has alleviated some of my concerns about the rigor of the experiments. I will raise my rate to 6.

Answer:
en