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 re-implement the model proposed in BIBREF3, and use it as a baseline for our problem. The rationale behind choosing this particular model as a baseline is it's proven good predictive performance on multilingual text classification. 
 Question: What is their baseline model?
Example Output: the model proposed in BIBREF3

Example Input: 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?
Example Output:  simple lookup table embeddings learned from scratch using high-performance contextual embeddings, which are ELMo BIBREF11, BERT BIBREF16 and ClinicalBERT BIBREF13

Example Input:  Our model improves the F1 score by almost 2%, which corresponds to a 12.3% error rate reduction. 
 Question: How better is proposed model compared to baselines?
Example Output:
 improves the F1 score by almost 2%, which corresponds to a 12.3% error rate reduction