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: Se trata de un articulo bastante particular que propone una extension a los modelos de datos existentes considerando la idea de "sistema" (de objetos). Para esto se propone un lenguaje (LATIN) y un modelo de datos universal?, MED, basado en un "volumen esferico" con dos polos "mayor" y "diario" basados en ideas contables. Este es, a mi juicio, el aspecto mas critico de la propuesta, ya que surge la duda, de si todo sistema deberia calzar en este esquema, o estamos hablando de un conjunto particular de casos. Las ideas posteriores son interesante y, al menos, generan un quiebre con respecto a las ideas clasicas de la orientacion a objetos. Cito la idea de sistema propuesta: "un sistema es un conjunto de clases enlazadas e inter-actuantes segun un mismo metodo, el cual no es particular a cada clase sino emergente de todas ellas". Otras ideas asocian transaccion con conjunto referencial, macros, tipos, instancias, ... lo que en terminos globales constituyen un nuevo cuerpo de ideas. No habiendo en las referencias alguna cita a algun articulo anterior del autor que trate estas ideas, me parece que puede llegar a ser interesante escuchar esta propuesta y debatir al respecto. En terminos de formalidad, el articulo no cumple cabalmente con el formato del congreso, y el numero de paginas excede las ocho solicitadas.

SOLUTION: es

PROBLEM: - 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?)

SOLUTION: en

PROBLEM: The paper provides a first study of customized precision hardware for large convolutional networks, namely alexnet, vgg and googlenet. It shows that it is possible to achieve larger speed-ups using floating-point precision (up to 7x) when using fewer bits, and better than using fixed-point representations. The paper also explores predicting custom floating-point precision parameters directly from the neural network activations, avoiding exhaustive search, but i could not follow this part. Only the activations of the last layer are evaluated, but on what data ? On all the validation set ? Why would this be faster than computing the classification accuracy ? The results should be useful for hardware manufacturers, but with a catch. All popular convolutional networks now use batch normalization, while none of the evaluated ones do. It may well be that the conclusions of this study will be completely different on batch normalization networks, and fixed-point representations are best there, but that remains to be seen. It seems like something worth exploring. Overall there is not a great deal of novelty other than being a useful study on numerical precision trade-offs at neural network test time. Training time is also something of interest. There are a lot more researchers trying to train new networks fast than trying to evaluate old ones fast. I am also no expert in digital logic design, but my educated guess is that this paper is marginally below the acceptance threshold.

SOLUTION:
en