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]: The second, Continuous Bag-of-Words (CBOW), the model is given the sequence of words without the middle one and attempts to predict this omitted word.  
 Question: Which word embeddings are analysed?
[A]: Continuous Bag-of-Words (CBOW)


[Q]: We first conduct experiments to compare the performance of FlowSeq with strong baseline models, including NAT w/ Fertility BIBREF6, NAT-IR BIBREF7, NAT-REG BIBREF25, LV NAR BIBREF26, CTC Loss BIBREF27, and CMLM BIBREF8. 
 Question: What non autoregressive NMT models are used for comparison?
[A]: NAT w/ Fertility NAT-IR NAT-REG LV NAR CTC Loss CMLM


[Q]: To account for sequences of words and characters that might carry useful information, we extracted word unigrams, bigrams, and trigrams as features. 
 Question: Which features do they use to model Twitter messages?
[A]:
word unigrams, bigrams, and trigrams