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.

Example Input: As a baseline for the RNN models, we apply a uni-directional RNN which predicts the relation after processing the whole sentence. 
 Question: Which variant of the recurrent neural network do they use?
Example Output: uni-directional RNN

Example Input: EmotionLines BIBREF6 is a dialogue dataset composed of two subsets, Friends and EmotionPush, according to the source of the dialogues. 
 Question: what datasets were used?
Example Output: Friends EmotionPush

Example Input: We train Word2Vec with vector size $d_\mathrm {W2V} = d_\mathrm {LM} = 768$ on PubMed+PMC (see Appendix for details). Then, we follow the procedure described in Section SECREF3 to update the wordpiece embedding layer and tokenizer of general-domain BERT. 
 Question: What in-domain text did they use?
Example Output:
PubMed+PMC