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.

[EX Q]: Our model differs by learning the subword vectors and resulting representation jointly as weighted factorization of a word-context co-occurrence matrix is performed.
Question: Which matrix factorization methods do they use?
[EX A]: weighted factorization of a word-context co-occurrence matrix

[EX Q]: The datasets used for training, validation and testing contain sentences extracted from the Europarl corpus BIBREF1 and SoNaR corpus BIBREF2. The Europarl corpus is an open-source parallel corpus containing proceedings of the European Parliament. The Dutch section consists of 2,333,816 sentences and 53,487,257 words. The SoNaR corpus comprises two corpora: SONAR500 and SONAR1. The SONAR500 corpus consists of more than 500 million words obtained from different domains.
Question: What are the sizes of both datasets?
[EX A]: The Dutch section consists of 2,333,816 sentences and 53,487,257 words. The SONAR500 corpus consists of more than 500 million words obtained from different domains.

[EX Q]: This section introduces our probabilistic model that infers keyword expectation and trains the target model simultaneously.
Question: What type of classifiers are used?
[EX A]:
probabilistic model