TASK DEFINITION: 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].
PROBLEM: El documento refleja que el equipo de trabajo cuenta con cierto dominio en la tecnología necesaria para alcanzar los objetivos planteados, sin embargo no está bien redactado, carece de sustento en trabajos previos, la aplicación no está bien descrita. Se recomienda también cuidar la redacción, concretar las ideas y cuidar la estructura en el documento. Se recomienda además una revisión profunda buscando la síntesis y profundizar en los detalle de la propuesta software que describen, y también se recomienda aplicar una metodología, describirla y describir la validación de la herramienta descrita (generar por ejemplo casos de prueba y describir la validación).

SOLUTION: es

PROBLEM: This paper proposes to use an encoder-decoder framework for keyphrase generation. Experimental results show that the proposed model outperforms other baselines if supervised data is available.  - Strengths: The paper is well-organized and easy to follow (the intuition of the proposed method is clear). It includes enough details to replicate experiments. Although the application of an encoder-decoder (+ copy mechanism) is straightforward, experimental results are reasonable and support the claim (generation of absent keyphrases) presented in this paper.  - Weaknesses: As said above, there is little surprise in the proposed approach. Also, as described in Section 5.3, the trained model does not transfer well to new domain (it goes below unsupervised models). One of the contribution of this paper is to maintain training corpora in good quantity and quality, but it is not (explicitly) stated.  - General Discussion: I like to read the paper and would be pleased to see it accepted. I would like to know how the training corpus (size and variation) affects the performance of the proposed method. Also, it would be beneficial to see the actual values of p_g and p_c (along with examples in Figure 1) in the CopyRNN model. From my experience in running the CopyNet, the copying mechanism sometimes works unexpectedly (not sure why this happens).

SOLUTION: en

PROBLEM: This paper presents several weakly supervised methods for developing NERs. The methods rely on some form of projection from English into another language. The overall approach is not new and the individual methods proposed are improvements of existing methods. For an ACL paper I would have expected more novel approaches.  One of the contributions of the paper is the data selection scheme. The formula used to calculate the quality score is quite straightforward and this is not a bad thing. However, it is unclear how the thresholds were calculated for Table 2. The paper says only that different thresholds were tried. Was this done on a development set? There is no mention of this in the paper. The evaluation results show clearly that data selection is very important, but one may not know how to tune the parameters for a new data set or a new language pair.   Another contribution of the paper is the combination of the outputs of the two systems developed in the paper. I tried hard to understand how it works, but the description provided is not clear.   The paper presents a number of variants for each of the methods proposed. Does it make sense to combine more than two weakly supervised systems? Did the authors try anything in this direction.  It would be good to know a bit more about the types of texts that are in the "in-house" dataset.

SOLUTION:
en