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].

Input: Consider Input: - Strengths: Zero-shot relation extraction is an interesting problem. The authors have created a large dataset for relation extraction as question answering which would likely be useful to the community.  - Weaknesses: Comparison and credit to existing work is severely lacking. Contributions of the paper don't seen particularly novel.  - General Discussion:  The authors perform relation extraction as reading comprehension. In order to train reading comprehension models to perform relation extraction, they create a large dataset of 30m “querified” (converted to natural language) relations by asking mechanical turk annotators to write natural language queries for relations from a schema. They use the reading comprehension model of Seo et al. 2016, adding the ability to return “no relation,” as the original model must always return an answer. The main motivation/result of the paper appears to be that the authors can perform zero-shot relation extraction, extracting relations only seen at test time.  This paper is well-written and the idea is interesting. However, there are insufficient experiments and comparison to previous work to convince me that the paper’s contributions are novel and impactful.  First, the authors are missing a great deal of related work: Neelakantan at al. 2015 ([Link] perform zero-shot relation extraction using RNNs over KB paths. Verga et al. 2017 ([Link] perform relation extraction on unseen entities. The authors cite Bordes et al. ([Link] who collect a similar dataset and perform relation extraction using memory networks (which are commonly used for reading comprehension). However, they merely note that their data was annotated at the “relation” level rather than at the triple (relation, entity pair) level… but couldn’t Bordes et al. have done the same in their annotation? If there is some significant difference here, it is not made clear in the paper. There is also a NAACL 2016 paper ([Link] which performs relation extraction using a new model based on memory networks… and I’m sure there are more. Your work is so similar to much of this work that you should really cite and establish novelty wrt at least some of them as early as the introduction -- that's how early I was wondering how your work differed, and it was not made clear.  Second, the authors neither 1) evaluate their model on another dataset or 2) evaluate any previously published models on their dataset. This makes their empirical results extremely weak. Given that there is a wealth of existing work that performs the same task and the lack of novelty of this work, the authors need to include experiments that demonstrate that their technique outperforms others on this task, or otherwise show that their dataset is superior to others (e.g. since it is much larger than previous, does it allow for better generalization?)

Output: en


Input: Consider Input: This paper proposes a novel strategy for zero-resource translation where (source, pivot) and (pivot, target) parallel corpora are available. A teacher model for p(target|pivot) is first trained on the (pivot, target) corpus, then a student model for p(target|source) is trained to minimize relative entropy with respect to the teacher on the (source, pivot) corpus. When using word-level relative entropy over samples from the teacher, this approach is shown to outperform previous variants on standard pivoting, as well as other zero-resource strategies.  This is a good contribution: a novel idea, clearly explained, and with convincing empirical support. Unlike some previous work, it makes fairly minimal assumptions about the nature of the NMT systems involved, and hence should be widely applicable.  I have only a few suggestions for further experiments. First, it would be interesting to see how robust this approach is to more dissimilar source and pivot languages, where intuitively the true p(target|source) and p(target|pivot) will be further apart. Second, given the success of introducing word-based diversity, it was surprising not to see a sentence n-best or sentence-sampling experiment. This would be more costly, but not much more so since you’re already doing beam search with the teacher. Finally, related to the previous, it might be interesting to explore transition from word-based diversity to sentence-based as the student converges and no longer needs the signal from low-probability words.  Some further comments:  line 241: Despite its simplicity -> Due to its simplicity  277: target sentence y -> target word y  442: I assume that K=1 and K=5 mean that you compare probabilities of the most probable and 5 most probable words in the current context. If so, how is the current context determined - greedily or with a beam?  Section 4.2. The comparison with an essentially uniform distribution doesn’t seem very informative here: it would be extremely surprising if p(y|z) were not significantly closer to p(y|x) than to uniform. It would be more interesting to know to what extent p(y|z) still provides a useful signal as p(y|x) gets better. This would be easy to measure by comparing p(y|z) to models for p(y|x) trained on different amounts of data or for different numbers of iterations. Another useful thing to explore in this section would be the effect of the mode approximation compared to n-best for sentence-level scores.  555: It’s odd that word beam does worse than word greedy, since word beam should be closer to word sampling. Do you have an explanation for this?  582: The claimed advantage of sent-beam here looks like it may just be noise, given the high variance of these curves.

Output: en


Input: Consider Input: El trabajo presenta una alternativa viable para resolver el problema del cambio de las reglas de negocio.  Se puede mejorar agregando algo más de background al tema de aspectos, dado que se asume un conocimiento previo de este tema, por lo que el paper no es autocontenido.


Output: es