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: The library design of Torch-Struct follows the distributions API used by both TensorFlow and PyTorch BIBREF29.
Question: Does API provide ability to connect to models written in some other deep learning framework?

A: Yes
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Q: We crowdsourced multiple-choice questions in two parts, encouraging workers to be imaginative and varied in their use of language. First, workers were given a seed qualitative relation q+/-( INLINEFORM0 ) in the domain, expressed in English (e.g., "If a surface has more friction, then an object will travel slower"), and asked to enter two objects, people, or situations to compare.
Question: Do all questions in the dataset allow the answers to pick from 2 options?

A: Yes
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Q: We use datasets from the NIST DUC-05, DUC-06 and DUC-07 shared tasks BIBREF7, BIBREF19, BIBREF20. Given a question and a cluster of newswire documents, the contestants were asked to generate a 250-word summary answering the question. DUC-05 contains 1,600 summaries (50 questions x 32 systems); in DUC-06, 1,750 summaries are included (50 questions x 35 systems); and DUC-07 has 1,440 summaries (45 questions x 32 systems).
Question: What dataset do they use?

A:
datasets from the NIST DUC-05, DUC-06 and DUC-07 shared tasks
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