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].

El trabajo presenta un estudio del estado del arte en lo que respecta a sistemas de robotica cooperativa de multi-robots. El foco de la revision es la arquitectura de control presente en aquellos sistemas. El abstract en ingles debe ser revisado en su redaccion, en especial el uso de palabras, como "giving"->"providing", "panoramic"->"general", etc. La introduccion no entrega un mayor aporte a la estructura del documento. Se habla un poco de la investigacion asociada al tema, pero con un uso muy bajo en referencias y la seccion termina abruptamente sin explicar metodologia o contribuciones del trabajo. El trabajo entrega un "planteamiento del problema" muy poco claro y que no provee mayor relevancia. Mas aun, ademas de identificar algunas aproximaciones al contexto, el trabajo no plantea ninguna solucion, alternativa, categorizacion, marco de trabajo o aporte en general al (poco claro) problema presentado. En resumen, el trabajo realiza una recopilacion relativamente adecuada de informacion respecto a sistemas de robotica cooperativa de multi-robots, sin embargo, mas alla de eso, no se revela un real aporte o relevancia desde el punto de vista cientifico al trabajo. Si estos puntos fueran mas claros, si se presentara de forma directa la metodologia, y si se presentara un producto concreto de investigacion, este trabajo podria ser aceptado, pero en su estado actual no se recomienda.
es

This paper makes a valuable contribution to provide a more clear understanding of generative adversarial network (GAN) training procedure. With the new insight of the training dynamics of GAN, as well as its variant, the authors reveal the reason that why the gradient is either vanishing in original GAN or unstable in its variant. More importantly, they also provide a way to avoid such difficulties by introducing perturbation. I believe this paper will inspire more principled research in this direction. I am very interested in the perturbation trick to avoid the gradient instability and vanishment. In fact, this is quite related to dropout trick in where the perturbation can be viewed as Bernoulli distribution. It will be great if the connection can be discussed. Besides the theoretical analysis, is there any empirical study to justify this trick? Could you please add some experiments like Fig 2 and 3 for the perturbated GAN for comparison?
en

- Strengths: Improves over the state-of-the-art. Method might be applicable for other domains. - Weaknesses: Not much novelty in method. Not quite clear if data set is general enough for other domains. - General Discussion: This paper describes a rule-based method for generating additional weakly labeled data for event extraction. The method has three main stages. First, it uses Freebase to find important slot fillers for matching sentences in Wikipedia (using all slot fillers is too stringent resulting in too few matches). Next, it uses FrameNet to to improve reliability of labeling trigger verbs and to find nominal triggers. Lastly, it uses a multi-instance learning to deal with the noisily generated training data. What I like about this paper is that it improves over the state-of-the-art on a non-trival benchmark. The rules involved don't seem too obfuscated, so I think it might be useful for the practitioner who is interested to improve IE systems for other domains. On the other hand, some some manual effort is still needed, for example for mapping Freebase event types to ACE event types (as written in Section 5.3 line 578). This also makes it difficult for future work to calibrate apple-to-apple against this paper. Apart from this, the method also doesn't seem too novel. Other comments: - I'm also concern with the generalizability of this method to other domains. Section 2 line 262 says that 21 event types are selected from Freebase. How are they selected? What is the coverage on the 33 event types in the ACE data. - The paper is generally well-written although I have some suggestions for improvement. Section 3.1 line 316 uses "arguments liked time, location...". If you mean roles or arguments, or maybe you want to use actual realizations of time and location as examples. There are minor typos, for e.g. line 357 is missing a "that", but this is not a major concern I have for this paper.
en