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.

[Q]: We use the Bayesian model of garg2012unsupervised as our base monolingual model. The semantic roles are predicate-specific.
Question: What does an individual model consist of?
[A]: Bayesian model of garg2012unsupervised as our base monolingual model

[Q]: 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?
[A]: 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.

[Q]: Our model achieves a 33.33 average of ROUGE-1, ROUGE-2 and ROUGE-L on the CNN/Daily Mail, which is state-of-the-art.
Question: What is the ROUGE score of the highest performing model?
[A]:
33.33 average of ROUGE-1, ROUGE-2 and ROUGE-L