Task: Language Identification
Input (Spanish, English): The paper describes an idea to learn phrasal representation and facilitate them in RNN-based language models and neural machine translation  -Strengths:  The  idea to incorporate phrasal information into the task is interesting.  - Weaknesses:  - The description is hard to follow. Proof-reading by an English native speaker would benefit the understanding - The evaluation of the approach has several weaknesses  - General discussion  - In Equation 1 and 2 the authors mention a phrase representation give a fix-length word embedding vector. But this is not used in the model. The representation is generated based on an RNN. What the propose of this description? - Why are you using GRU for the Pyramid and LSTM for the sequential part? Is the combination of two architectures a reason for your improvements? - What is the simplified version of the GRU? Why is it performing better? How is it performing on the large data set? - What is the difference between RNNsearch (groundhog) and RNNsearch(baseline) in Table 4? -  What is the motivation for only using the ending phrases and e.g. not using the starting phrases? - Did you use only the pyramid encoder? How is it performing? That would be a more fair comparison since it normally helps to make the model more complex. - Why did you run RNNsearch several times, but PBNMT only once?  - Section 5.2: What is the intent of this section
Output (English): en