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: Embedding: We developed different variations of our models with a simple lookup table embeddings learned from scratch and using high-performance contextual embeddings, which are ELMo BIBREF11, BERT BIBREF16 and ClinicalBERT BIBREF13 (trained and provided by the authors). 
 Question: What embeddings are used?

A:  simple lookup table embeddings learned from scratch using high-performance contextual embeddings, which are ELMo BIBREF11, BERT BIBREF16 and ClinicalBERT BIBREF13
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Q: We decided to use sentences involving at least one race- or gender-associated word. The sentences were intended to be short and grammatically simple. We also wanted some sentences to include expressions of sentiment and emotion, since the goal is to test sentiment and emotion systems. 
 Question: What criteria are used to select the 8,640 English sentences?

A: Sentences involving at least one race- or gender-associated word,  sentence  have to be short and grammatically simple,  sentence have to  include expressions of sentiment and emotion.
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Q: For each qualifying diagnosis tweet we retrieve the timeline of the corresponding Twitter user using the Twitter user_timeline API endpoint . Subsequently, we remove all non-English tweets (Twitter API machine-detected“lang” field), all retweets, and tweets that contain “diagnos*” or “depress*”, but not a valid diagnosis statement. The resulting Depressed cohort contains 1,207 individuals and 1,759,644 tweets ranging from from May 2008 to September 2018. 
 Question: Do they report results only on English datasets?

A:
Yes
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