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].

This paper presents a dialogue agent where the belief tracker and the dialogue manager are jointly optimised using the reinforce algorithm. It learns from interaction with a user simulator. There are two training phases. The first is an imitation learning phase where the system is initialised using supervising learning from a rule-based model. Then there is a reinforcement learning phase where the system has jointly been optimised using the RL objective. - Strengths: This paper presents a framework where a differentiable access to the KB is integrated in the joint optimisation. This is the biggest contribution of the paper. - Weaknesses: Firstly, this is not a truly end-to-end system considering the response generation was handcrafted rather than learnt. Also, their E2E model actually overfits to the simulator and performs poorly in human evaluation. This begs the question whether the authors are actually selling the idea of E2E learning or the soft-KB access. The soft-KB access actually brings consistent improvement, however the idea of end-to-end learning not so much. The authors tried to explain the merits of E2E in Figure 5 but I also fail to see the difference. In addition, the authors didn't motivate the reason for using the reinforce algorithm which is known to suffer from high variance problem. They didn't attempt to improve it by using a baseline or perhaps considering the natural actor-critic algorithm which is known to perform better. - General Discussion: Apart from the mentioned weaknesses, I think the experiments are solid and this is generally an acceptable paper. However, if they crystallised the paper around the idea which actually improves the performance (the soft KB access) but not the idea of E2E learning the paper would be better.
en

Se presenta el desarrollo de un prototipo para una herramienta de asignacion automatica de requisitos de software entre los desarrolladores. Esta herramienta puede ser muy util para los equipos de desarrollo de software. Se trata de realizar un aporte mediante esta herramienta a la calidad de los productos desarrollados. No se explica cual es la heuristica utilizada para la asignacion de requisitos a los programadores. No se presentan un estudio de las herramientas de gestion de requisitos existentes, en donde se demuestre que no existen herramientas alternativas a la presentada. Y en caso de existir alternativas, justificar por que desarrollar una nueva. No se explica claramente las funcionalidades de la herramienta. No hay evidencia de la mejora del proceso al incluir esta herramienta, solo se dice en las conclusiones que "es perceptible el cambio en la calidad del proceso" Las referencias bibliograficas utilizadas son en su mayoria paginas web, no se nota una revision bibliografica en bases de datos especializadas. Las figuras no se distinguen bien. Las referencias bibliograficas no cumplen el formato establecido. Debe redactar en tercera persona.
es

This paper presents a system approach to combine multiple modalities to perform classification in a practical scenario (e-commerce). In general, I find the proposed approach in the paper sound and solid, but do not see novelty in the paper: feature fusion and decision time fusion are both standard practices in multi-modal analysis, and the rest of the paper offers no surprise in implementing such approaches. This seems to be a better fit for venues that focus more on production systems, and seems to be a bad fit for ICLR where the focus is more on research of novel algorithms and theories.
en