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].

Ex Input:
- Strengths:  This paper proposes to apply NLP to speech transcripts (narratives and descriptions) in order to identify patients with MCI (mild cognitive impairment, ICD-10 code F06.7). The authors claim that they were able to distinguish between healthy control participants and patients with MCI (lines 141-144). However in the conclusion, lines 781-785, they say that “… accuracy ranging from 60% to 85% …. means that it is not easy to distinguish between healthy subjects and those with cognitive impairments”. So the paper beginning is more optimistic than the conclusion but anyway the message is encouraging and the reader becomes curious to see more details about what has been actually done.  The corpus submitted in the dataset is constructed for 20 healthy patients and 20 control participants only (20+20), and it is non-understandable for people who do not speak Portuguese. It would be good to incorporate more technological details in the article and probably to include at least one example of a short transcript that is translated to English, and eventually a (part of a) sample network with embeddings for this transcript.  - Weaknesses:  The paper starts with a detailed introduction and review of relevant work. Some of the cited references are more or less NLP background so they can be omitted e.g. (Salton 1989) in section 4.2.3. Other references are not directly related to the topic e.g. “sentiment classification” and “pedestrian detection in images”, lines 652-654, and they can be omitted too. In general lines 608-621, section 4.2.3 can be shortened as well etc. etc. The suggestion is to compress the first 5 pages, focusing the review strictly on the paper topic, and consider the technological innovation in more detail, incl. samples of English translations of the ABCD and/or Cindarela narratives.  The relatively short narratives in Portuguese esp. in ABCD dataset open the question how the similarities between words have been found, in order to construct word embeddings. In lines 272-289 the authors explain that they generate word-level networks from continuous word representations. What is the source for learning the continuous word representations; are these the datasets ABCD+Cinderella only, or external corpora were used? In lines 513-525 it is written that sub-word level (n-grams) networks were used to generate word embeddings. Again, what is the source for the training? Are we sure that the two kinds of networks together provide better accuracy? And what are the “out-of-vocabulary words” (line 516), from where they come?  - General Discussion:  It is important to study how NLP can help to discover cognitive impairments; from this perspective the paper is interesting. Another interesting aspect is that it deals with NLP for Portuguese, and it is important to explain how one computes embeddings for a language with relatively fewer resources (compared to English).   The text needs revision: shortening sections 1-3, compressing 4.1 and adding more explanations about the experiments. Some clarification about the NURC/SP N. 338 EF and 331 D2 transcription norms can be given.  Technical comments:  Line 029: ‘… as it a lightweight …’ -> shouldn’t this be ‘… as in a lightweight …’  Line 188: PLN -> NLP  Line 264: ‘out of cookie out of the cookie’ – some words are repeated twice   Table 3, row 2, column 3: 72,0 -> 72.0  Lines 995-996: the DOI number is the same as the one at lines 1001-1002; the link behind the title at lines 992-993 points to the next paper in the list

Ex Output:
en


Ex Input:
This paper describes how to approximate the FastText approach such that its memory footprint is reduced by several orders of magnitude, while preserving its classification accuracy. The original FastText approach was based on a linear classifier on top of bag-of-words embeddings. This type of method is extremely fast to train and test, but the model size can be quite large.  This paper focuses on approximating the original approach with lossy compression techniques. Namely, the embeddings and classifier matrices A and B are compressed with Product Quantization, and an aggressive dictionary pruning is carried out. Experiments on various datasets (either with small or large number of classes) are conducted to tune the parameters and demonstrate the effectiveness of the approach. With a negligible loss in classification accuracy, an important reduction in term of model size (memory footprint) can be achieved, in the order of 100~1000 folds compared to the original size.  The paper is well written overall. The goal is clearly defined and well carried out, as well as the experiments. Different options for compressing the model data are evaluated and compared (e.g. PQ vs LSH), which is also interesting. Nevertheless the paper does not propose by itself any novel idea for text classification. It just focuses on adapting existing lossy compression techniques, which is not necessarily a problem. Specifically, it introduces:   - a straightforward variant of PQ for unnormalized vectors,   - dictionary pruning is cast as a set covering problem (which is NP-hard), but a greedy approach is shown to yield excellent results nonetheless,   - hashing tricks and bloom filter are simply borrowed from previous papers.  These techniques are quite generic and could as well be used in other works.    Here are some minor problems with the paper:    - it is not made clear how the full model size is computed. What is exactly in the model? Which proportion of the full size do the A and B matrices, the dictionary, and the rest, account for? It is hard to follow where is the size bottleneck, which also seems to depend on the target application (i.e. small or large number of test classes). It would have been nice to provide a formula to calculate the total model size as a function of all parameters (k,b for PQ and K for dictionary, number of classes).      - some parts lack clarity. For instance, the greedy approach to prune the dictionary is exposed in less than 4 lines (top of page 5), though it is far from being straightforward. Likewise, it is not clear why the binary search used for the hashing trick would introduce an overhead of a few hundreds of KB.     Overall this looks like a solid work, but with potentially limited impact research-wise.

Ex Output:
en


Ex Input:
This paper addresses the practical problem of generating rare or unseen words in the context of language modeling. Since language follows a Zipf’s law, most approaches limit the vocabulary (because of computation reasons) and hence rare words are often mapped to a UNK token. Rare words are especially important in context of applications such as question answering. MT etc. This paper proposes a language modeling technique which incorporates facts from knowledge bases (KBs) and thus has the ability to generate (potentially unseen) words from KBs. This paper also releases a dataset by aligning words with Freebase facts and corresponding Wikipedia descriptions.  The model first selects a KB fact based on the previously generated words and facts. Based on the selected fact, it then predicts whether to generate a word based on the vocabulary or to output a symbolic word from the KB. For the latter, the model is trained to predict the position of the word from the fact description.  Overall the paper could use some rewriting especially the notations in section 3. The experiments are well executed and they definitely get good results. The heat maps at the end are very insightful.   Comments  This contributions of this paper would be much stronger if it showed improvements in a practical applications such as Question Answering (although the paper clearly mentions that this technique could be applied to improve QA) In section 3, it is unclear why the authors refer the entity as a ‘topic'. This makes the text a little confusing since a topic can also be associated with something abstract, but in this case the topic is always a freebase entity.  Is it really necessary to predict a fact at every step before generating a word. In other words, how many distinct facts on average does the model choose to generate a sentence. Intuitively a natural language sentence would be describe few facts about an entity. If the fact generation step could be avoided (by adding a latent variable which decides if the fact should be generated or not), the model will also be faster. In equation 2, the model has to make a hard decision to choose the fact. For this to be end to end trained, every word needs to be annotated with a corresponding fact which might not be always a realistic scenario. For e.g., in domains such as social media text. Learning position embeddings for copying knowledge words seems a little counter-intuitive. Does the sequence of knowledge words follow any particular structure like word O_2 is always the last name (e.g. Obama). It would also be nice to compare to char-level LM's which inherently solves the unknown token problem.

Ex Output:

en