In this task, you will be presented with a context from an academic paper and a question separated with a 
. You have to answer the question based on the context.

[Q]: In fact, realizing the necessity of large text corpus for Sindhi, we started this research by collecting raw corpus from multiple web resource using web-scrappy framwork for extraction of news columns of daily Kawish and Awami Awaz Sindhi newspapers, Wikipedia dumps, short stories and sports news from Wichaar social blog, news from Focus Word press blog, historical writings, novels, stories, books from Sindh Salamat literary websites, novels, history and religious books from Sindhi Adabi Board and tweets regarding news and sports are collected from twitter. 
 Question: How is the data collected, which web resources were used?
[A]: daily Kawish and Awami Awaz Sindhi newspapers Wikipedia dumps short stories and sports news from Wichaar social blog news from Focus Word press blog historical writings, novels, stories, books from Sindh Salamat literary website novels, history and religious books from Sindhi Adabi Board  tweets regarding news and sports are collected from twitter


[Q]: The results are shown in Fig. FIGREF87 for different numbers of topics, where we can see that the proposed model outperforms all the baselines, being the svi version the one that performs best. In order to assess the computational advantages of the stochastic variational inference (svi) over the batch algorithm, the log marginal likelihood (or log evidence) was plotted against the number of iterations. Fig. FIGREF88 shows this comparison. Not surprisingly, the svi version converges much faster to higher values of the log marginal likelihood when compared to the batch version, which reflects the efficiency of the svi algorithm. 
 Question: what are the advantages of the proposed model?
[A]: he proposed model outperforms all the baselines, being the svi version the one that performs best. the svi version converges much faster to higher values of the log marginal likelihood when compared to the batch version, which reflects the efficiency of the svi algorithm.


[Q]: In the sentiment classification task, we tried our model on the following public datasets. MR Movie reviews with one sentence each. IMDB Movie reviews from IMDB website, where each movie review is labeled with binary classes, either positive or negative BIBREF19. SUBJ$^1$ Movie review labeled with subjective or objective BIBREF20. We also tested our CRU model in the cloze-style reading comprehension task. We carried out experiments on the public datasets: CBT NE/CN BIBREF25. 
 Question: What datasets are used for testing sentiment classification and reading comprehension?
[A]:
CBT NE/CN MR Movie reviews IMDB Movie reviews SUBJ