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.

Twitter data: We used the Twitter API to scrap tweets with hashtags. All non-English tweets were filtered out by the API. 
 Question: Do they evaluate only on English datasets?
Yes

For instance, we observed that winter words were together and far away from summer ones. Week days were grouped as well and far from week-end days. 
 Question: What geometric properties do embeddings display?
Winter and summer words formed two separate clusters. Week day and week-end day words also formed separate clusters.

Since our objective is to compare character-based and word-based approaches, we have also implemented a simple word-level encoder for tweets. The input tweet is first split into tokens along white-spaces. A more sophisticated tokenizer may be used, but for a fair comparison we wanted to keep language specific preprocessing to a minimum. The encoder is essentially the same as tweet2vec, with the input as words instead of characters. A lookup table stores word vectors for the $V$ (20K here) most common words, and the rest are grouped together under the `UNK' token. 
 Question: what is the word level baseline they compare to?

a simple word-level encoder with the input as words instead of characters