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.

[Q]: We focus primarily on the word vector representations (word embeddings) created specifically using the twitter dataset. GloVe BIBREF13 is an unsupervised learning algorithm for obtaining vector representations for words. 200-dimensional GloVe embeddings trained on 2 Billion tweets are integrated. Edinburgh embeddings BIBREF14 are obtained by training skip-gram model on Edinburgh corpus BIBREF15 . Since tweets are abundant with emojis, Emoji embeddings BIBREF16 which are learned from the emoji descriptions have been used. Embeddings for each tweet are obtained by summing up individual word vectors and then dividing by the number of tokens in the tweet.
[A]: what pretrained word embeddings were used?

[Q]: We use the morphological tagging datasets provided by the Universal Dependencies (UD) treebanks (the concatenation of the $4^ ext{th}$ and $6^ ext{th}$ columns of the file format) BIBREF13 .
[A]: On which dataset is the experiment conducted?

[Q]: Our desiderata for the task collection were: sufficient diversity, existence of fairly large datasets for training, and success as standalone training objectives for sentence representations.

Multi-task training setup
[A]:
Which model architecture do they for sentence encoding?