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: We use Waseem and Hovy BIBREF5, Davidson et al. BIBREF9, and Waseem et al. BIBREF10 as baselines and compare the results with our different fine-tuning strategies using pre-trained BERTbase model.  
 Question: What existing approaches do they compare to?

A: Waseem and Hovy BIBREF5, Davidson et al. BIBREF9, and Waseem et al. BIBREF10
****
Q: In order to represent individual sentences, we insert external [cls] tokens at the start of each sentence, and each [cls] symbol collects features for the sentence preceding it. We also use interval segment embeddings to distinguish multiple sentences within a document. This way, document representations are learned hierarchically where lower Transformer layers represent adjacent sentences, while higher layers, in combination with self-attention, represent multi-sentence discourse. Position embeddings in the original Bert model have a maximum length of 512; we overcome this limitation by adding more position embeddings that are initialized randomly and fine-tuned with other parameters in the encoder. 
 Question: What is novel about their document-level encoder?

A: Bert model have a maximum length of 512; we overcome this limitation by adding more position embeddings we insert external [cls] tokens at the start of each sentence, and each [cls] symbol collects features for the sentence preceding it document representations are learned hierarchically
****
Q: We obtain word vectors of size 300 from the learned word embeddings. To represent a Twitter profile, we retrieve word vectors for all the words that appear in a particular profile including the words appear in tweets, profile description, words extracted from emoji, cover and profile images converted to textual formats, and words extracted from YouTube video comments and descriptions for all YouTube videos shared in the user's timeline. Those word vectors are combined to compute the final feature vector for the Twitter profile. 
 Question: How in YouTube content translated into a vector format?

A:
words extracted from YouTube video comments and descriptions for all YouTube videos shared in the user's timeline
****