Teacher: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].
Teacher: Now, understand the problem? Solve this instance: - Strengths: The authors present a novel adaptation of encoder-decoder neural MT using an approach that starts and ends with characters, but in between works with representations of morphemes and characters. The authors release both their code as well as their final learned models for fr-en, cs-en, and en-cs. This is helpful in validating their work, as well as for others looking to replicate and extends this work. The system reported appears to produce translation results of reasonable quality even after the first training epoch, with continued progress in future epochs. The system appears to learn reasonable morphological tokenizations, and appears able to handle previously unseen words (even nonce words) by implicitly backing off to morphemes. - Weaknesses: In the paper, the authors do not explicitly state which WMT test and dev sets their results are reported on. This is problematic for readers wishing to compare the reported results to existing work (for example, the results at matrix.statmt.org). The only way this reviewer found to get this information was to look in the README of the code supplement, which indicates that the test set was newstest2015 and the dev test was newstest2013. This should have been explicitly described in the paper. The instructions given in the software README are OK, but not great. The training and testing sections each could be enhanced with explicit examples of how to run the respective commands. The software itself should respond to a --help flag, which it currently does not. The paper describes a 6-level architecture, but the diagram in Figure 2 appears to show fewer than 6 layers. What's going on? The caption should be more explicit, and if this figure is not showing all of the layers, then there should be a figure somewhere (even if it's in an appendix) showing all of the layers. The results show comparison to other character-based neural systems, but do not show state-of-the-art results for other types of MT system. WMT (and matrix.statmt.org) has reported results for other systems on these datasets, and it appears that the state-of-the-art is much higher than any of the results reported in this paper. That should be acknowledged, and ideally should be discussed. There are a handful of minor English disfluencies, misspellings, and minor LaTeX issues, such as reverse quotation marks. These should be corrected. - General Discussion: Paper is a nice contribution to the existing literature on character-based neural MT.
Student:
en