You will be given a definition of a task first, then some input of the task.
In this task, you will be presented with a context from an academic paper and you have to write an answerable question based on the context. Your questions can be extractive, abstractive, or yes-no questions.

We used the public tool, word2vec, released by Mikolov-2013 to obtain the word embeddings. Their neural network approach is similar to the feed-forward neural networks BIBREF5 , BIBREF6 . In the Skip-gram model architecture we used, we have chosen 200 as the dimension of the obtained word vectors.
Output:
What type and size of word embeddings were used?