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: We carry out experiments on multiple language pairs including German-English, French-English, and Japanese-English.  
 Question: Which languages are used in the multi-lingual caption model?
Example Output: German-English, French-English, and Japanese-English

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: We compare our system to a variety of baselines. (1) Rank by the number of times a citation is mentioned in the document. (2) Rank by the number of times the citation is cited in the literature (citation impact). (3) Rank using Google Scholar Related Articles. (4) Rank by the TF*IDF weighted cosine similarity. (5) Rank using a learning-to-rank model trained on text similarity rankings. The first two baseline systems are models where the values are ordered from highest to lowest to generate the ranking. The idea behind them is that the number of times a citation is mentioned in an article, or the citation impact may already be good indicators of their closeness. The text similarity model is trained using the same features and methods used by the annotation model, but trained using text similarity rankings instead of the author's judgments. 
 Question: what were the baselines?
Example Output:
Rank by the number of times a citation is mentioned in the document  Rank by the number of times the citation is cited in the literature (citation impact).  Rank using Google Scholar Related Articles. Rank by the TF*IDF weighted cosine similarity.  ank using a learning-to-rank model trained on text similarity rankings