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.

Ex Input:
The task is set up to mimic (albeit, in an oversimplified manner) the input-output symbol alignments and local syntactic properties that models must learn in many natural language tasks, such as translation, tagging and summarization. 
 Question: Why does the proposed task a good proxy for the general-purpose sequence to sequence tasks?

Ex Output:
The task is set up to mimic (albeit, in an oversimplified manner) the input-output symbol alignments and local syntactic properties that models must learn in many natural language tasks, such as translation, tagging and summarization.


Ex Input:
In addition to the majority baseline, we also compare our results with a lexicon-based approach. For experimental results, we report majority baseline for each language where the majority baseline corresponds to a model's accuracy if it always predicts the majority class in the dataset. 
 Question: what are the baselines?

Ex Output:
majority baseline lexicon-based approach


Ex Input:
For answer retrieval, a dataset is created by INLINEFORM4 , which gives INLINEFORM5 accuracy and INLINEFORM6 coverage, respectively. 
 Question: Do they employ their indexing-based method to create a sample of a QA Wikipedia dataset?

Ex Output:

Yes