instruction:
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].
question:
El  trabajo "Liderazgo en adopción de TI en PYMEs ¿Sólo el involucramiento del propietario importa?" explora a través de la metodología IA la relación entre involucramiento del propietario y el éxito de la TI en PYME. Si bien es un buen trabajo, el texto puede ser mejorado en algunos aspectos de forma y fondo: 1.	Los autores deben declarar el objetivo del estudio para facilitar la lectura del trabajo. 2.	Lamentablemente los autores cometen un error importante al colocar a  D&M como modelo de aceptación a nivel individual ("Otros modelos de aceptación a nivel individual incluyen: el modelo de DeLone y McLean (D&M) [5]"). De hecho, a partir de ese error la redacción del texto confunde dimensiones del éxito del uso de sistemas de información (el modelo D&M) con modelos de adopción de tecnologías de información (TAM y sucesores). Los autores deben revisar D&M y ver las implicaciones para su trabajo de la correcta interpretación de este modelo. 3.	Asociado a lo anterior, ¿que entienden los autores por éxito del uso de sistemas de información? deben explicarlo, ¿los beneficios individuales y los beneficios organizaciones son parte de ellos?,  ¿es posible que otras dimensiones del éxito del uso de sistemas de información, como la calidad de la información y la calidad de servicio de la función informática, sean variables importantes para explorar en los dos proyectos estudiados?
answer:
es


question:
- 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?)
answer:
en


question:
El artículo detalla una propuesta de gestión de requisitos basada en KAIZEN. Los autores describen diferentes métodos y técnicas para complementar Kaizen  Entiendo que el principal aporte de este trabajo es, según los autores, complementar y mejorar Kaizen para elicitación y gestión de requisitos. Sin embargo, los autores del artículo presentan únicamente una construcción teórica de su propuesta.  Entiendo que al artículo le falta trabajo por desarrollar, con el fin de validar la propuesta. Sin embargo al ser un artículo que desarrollan seis investigadores, considero que debe incluir aspectos más aplicativos de la propuesta.
answer:

es