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 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.
[A]: en


[Q]: Me parece un trabajo interesante, principalmente por aportar evidencia empírica sobre técnicas de co-creación. Su contenido y estructura están técnicamente bien presentados. No obstante, el trabajo tiene omisiones relevantes para valorar su contribución científica. Por ejemplo, no presenta análisis estadístico de los datos arrojados. Solo expone una distribución de los resultados sin evidenciar significancia estadística. También extraño una sección (o en las conclusiones) sobre limitaciones a la validez del experimento. Creo que tiene varias amenazas a la validez que no han sido tratadas. Por nombrar algunas: no se explica si la asignación a los grupos fue aleatoriamente; el tiempo de las sesiones es distinta; el aprendizaje en sesiones anteriores, etc.  Otras sugerencias:  pág. 1	Título	Creo que no es adecuado el título. No representa el core del trabajo que es un experimento. pág. 4	Segunda columna	Errores en formato de viñetas  pág. 4	Tercera viñeta segunda columna	cada VEZ que  pág. 5	Última viñeta primera columna	paréntesis
[A]: es


[Q]: The authors propose a Gated Muiltimodal Unit to combine multi-modal information (visual and textual). They also collect a large dataset of movie summers and posters. Overall, the reviewers were quite positive, while AR4 points to related models and feels that the contribution in the current version is too weak for ICLR. The AC read the paper and the authors responses but tends to agree with AR4. The authors are encouraged to strengthen their work and resubmit to a future conference.
[A]:
en


