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: The authors apply the image captioning architecture of Xu et al. 2015 to video captioning. The model is extended to have attention over multiple layers of the ConvNet instead of just a single layer. Experiments on YouTube2Text, M-VAD and MSR-VTT show that this works better than only using one of the layers at a time. I think this is solid work on the level of a well-executed course project or a workshop paper. The model makes sense, it is adequately described, and the experiments show that attending over multiple layers works better than attending over any one layer in isolation. Unfortunately, I don't think there is enough to get excited about here from a technical perspective and it's not clear what value the paper brings to the community. Other aspects of the paper, such as including the hard attention component, don't seem to add to the paper but take up space. If the authors want to contribute a detailed, focused exploration of multi-level features this could become a more valuable paper, but in that case I would expect a much more thorough exploration of the choices and tradeoffs of different schemes without too many spurious aspects such as video features, hard attention, etc.
A:
en