Detailed Instructions: 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].
See one example below:
Problem: Este articulo no es un articulo de investigacion, ya que solo muestra como programar un robot mediante la herramienta de logica difusa. Este tema ya ha sido propuesto como solucion en navegacion de robots.
Solution: es
Explanation: This review is written in spanish. Hence, the label is 'es'.

Problem: - Strengths: 1. The idea of assigning variable-length document segments with dependent topics is novel. This prior knowledge is worth incorporated in the LDA-based framework. 2. Whereas we do not have full knowledge on recent LDA literature, we find the part of related work quite convincing. 3. The method proposed for segment sampling with O(M) complexity is impressive. It is crucial for efficient computation. - Weaknesses: 1. Compared to Balikas COLING16's work, the paper has a weaker visualization (Fig 5), which makes us doubt about the actual segmenting and assigning results of document. It could be more convincing to give a longer exemplar and make color assignment consistent with topics listed in Figure 4. 2. Since the model is more flexible than that of Balikas COLING16, it may be underfitting, could you please explain this more? - General Discussion: The paper is well written and structured. The intuition introduced in the Abstract and again exemplified in the Introduction is quite convincing. The experiments are of a full range, solid, and achieves better quantitative results against previous works. If the visualization part is stronger, or explained why less powerful visualization, it will be more confident. Another concern is about computation efficiency, since the seminal LDA work proposed to use Variational Inference which is faster during training compared to MCMC, we wish to see the author's future development.
Solution:
en