You will be given a definition of a task first, then some input of the task.
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].

Overall the paper address an important problem: how to evaluate more appropriately automatic dialogue responses given the fact that current practice to automatically evaluate (BLEU, METEOR, ...) is often insufficient and sometimes misleading. The proposed approach using an LSTM-based encoding of dialogue context, reference response and model response(s) that are then scored in a linearly transformed space. While the overall approach is simple it is also quite intuitiv and allows end-to-end training. As the authors rightly argue simplicity is a feature both for interpretation as well as for speed. The experimental section reports on quite a range of experiments that seem fine to me and aim to convince the reader about the applicability of the approach. As mentioned also by others more insights from the experiments would have been great. I mentioned an in-depth failure case analysis and I would also suggest to go beyond the current dataset to really show generalizability of the proposed approach. In my opinion the paper is somewhat weaker on that front that it should have been. Overall I like the ideas put forward and the approach seems sensible though and the paper can thus be accepted.
Output:
en