In this task, you will be presented with a context from an academic paper and you have to write an answerable question based on the context. Your questions can be extractive, abstractive, or yes-no questions.
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Question: E2E NLG challenge Dataset: The training set of the E2E challenge dataset which consists of 42K samples was partitioned into a 10K paired and 32K unpaired datasets by a random process.  The Wikipedia Company Dataset: The Wikipedia company dataset presented in Section SECREF18 was filtered to contain only companies having abstracts of at least 7 words and at most 105 words. The dataset was then divided into: a training set (35K), a development set (4.3K) and a test set (4.3K). The training set was also partitioned in order to obtain the paired and unpaired datasets. 

Answer: What non-annotated datasets are considered?


Question: Our corpus comprises a total of 6,127 scientific entities, including 2,112 Process, 258 Method, 2,099 Material, and 1,658 Data entities. 

Answer: How large is the dataset?


Question: The most intuitive way to represent syntactic information is to use individual dependency relations directly, like dependency head and dependency relation label, denoted as Dep and Rel for short. In order to preserve the structural information of dependency trees as much as possible, we take the syntactic path between candidate arguments and predicates in dependency trees as linguistic knowledge. Referring to BIBREF9, we use the Tree-based Position Feature (TPF) as Dependency Path (DepPath) and use the Shortest Dependency Path (SDP) as Relation Path (RelPath).

Answer:
What different approaches of encoding syntactic information authors present?