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: Following lebret2016neural, we used BLEU-4, NIST-4 and ROUGE-4 as the evaluation metrics. 
 Question: What metrics are used for evaluation?
Example Output: BLEU-4 NIST-4 ROUGE-4

Example Input: Using a set of semantically oriented tasks that require explicit semantic cross-lingual representations, we showed that mBERT contextual embeddings do not represent similar semantic phenomena similarly and therefore they are not directly usable for zero-shot cross-lingual tasks. 
 Question: What challenges this work presents that must be solved to build better language-neutral representations?
Example Output: contextual embeddings do not represent similar semantic phenomena similarly and therefore they are not directly usable for zero-shot cross-lingual tasks

Example Input: Baselines We thoroughly compare our approaches to the following baselines:

Direct source$\rightarrow $target: A standard NMT model trained on given source$\rightarrow $target parallel data.

Multilingual: A single, shared NMT model for multiple translation directions BIBREF6.

Many-to-many: Trained for all possible directions among source, target, and pivot languages.

Many-to-one: Trained for only the directions to target language, i.e., source$\rightarrow $target and pivot$\rightarrow $target, which tends to work better than many-to-many systems BIBREF27. 
 Question: What are multilingual models that were outperformed in performed experiment?
Example Output:
Direct source$\rightarrow $target: A standard NMT model trained on given source$\rightarrow $target Multilingual: A single, shared NMT model for multiple translation directions Many-to-many: Trained for all possible directions among source, target, and pivot languages Many-to-one: Trained for only the directions to target language