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].
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Question: This paper extends preceding works to create a mapping between the word embedding space of two languages. The word embeddings had been independently trained on monolingual data only, and various forms of bilingual information is used to learn the mapping. This mapping is then used to measure the precision of translations.  In this paper, the authors propose two changes: "CCA" and "inverted softmax".  Looking at Table 1, CCA is only better than Dina et al in 1 out of 6 cases (It/En @1).  Most of the improvements are in fact obtained by the introduction of the inverted softmax normalization.  Overall, I wonder which aspect of this paper is really new. You mention:  - Faruqui & Dyer 2014 already used CCA and dimensionality reduction  - Xing et al 2015 argued already that Mikolov's linear matrix should be orthogonal  Could you make clear in what aspect your work is different from Faruqui & Dyer 2014 (other the fact that you applied the method to measure translation precision) ?  Using cognates instead of a bilingual directory is a nice trick. Please explain how you obtained this list of cognates ? Obviously, this only works for languages with the same alphabet (for instance Greek and Russian are excluded)  Also, it seems to me that in linguistics the term "cognate" refers to words which have a common etymological origin - they don't necessarily have the same written form (e.g. night, nuit, noche, Nacht). Maybe, you should use a different term ? Those words are probably proper names in news texts.

Answer: en


Question: Review: Multimodal Word Distributions  - Strengths:  Overall a very strong paper.  - Weaknesses: The comparison against similar approaches could be extended.  - General Discussion:  The main focus of this paper is the introduction of a new model for learning multimodal word distributions formed from Gaussian mixtures for multiple word meanings. i. e. representing a word by a set of many Gaussian distributions.  The approach, extend the model introduced by Vilnis and McCallum (2014) which represented word as unimodal Gaussian distribution. By using a multimodal, the current approach attain the problem of polysemy.  Overall, a very strong paper, well structured and clear. The experimentation is correct and the qualitative analysis made in table 1 shows results as expected from the approach.  There’s not much that can be faulted and all my comments below are meant to help the paper gain additional clarity.   Some comments:   _ It may be interesting to include a brief explanation of the differences between the approach from Tian et al. 2014 and the current one. Both split single word representation into multiple prototypes by using a mixture model.   _ There are some missing citations that could me mentioned in related work as :  Efficient Non-parametric Estimation of Multiple Embeddings per Word in Vector Space Neelakantan, A., Shankar. J. Passos, A., McCallum. EMNLP 2014 Do Multi-Sense Embeddings Improve Natural Language Understanding? Li and Jurafsky, EMNLP 2015 Topical Word Embeddings. Liu Y., Liu Z., Chua T.,Sun M. AAAI 2015  _ Also, the inclusion of the result from those approaches in tables 3 and 4 could be interesting.   _ A question to the authors: What do you attribute the loss of performance of w2gm against w2g in the analysis of SWCS?  I have read the response.

Answer: en


Question: This paper presents a new dataset with annotations of products coming from online cybercrime forums. The paper is clear and well-written and the experiments are good. Every hypothesis is tested and compared to each other.  However, I do have some concerns about the paper:  1. The authors took the liberty to change the font size and the line spacing of the abstract, enabling them to have a longer abstract and to fit the content into the 8 pages requirement.  2. I don't think this paper fits the tagging, chunking, parsing area, as it is more an information extraction problem.  3. I have difficulties to see why some annotations such as sombody in Fig. 1 are related to a product.  4. The basic results are very basic indeed and - with all the tools available nowadays in NLP -, I am sure that it would have been possible to have more elaborate baselines without too much extra work.  5. Domain adaptation experiments corroborate what we already know about user-generated data where two forums on video games, e.g., may have different types of users (age, gender, etc.) leading to very different texts. So this does not give new highlights on this specific problem.

Answer:
en