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.

a) Parallel Scan Inference b) Vectorized Parsing c) Semiring Matrix Operations Torch-Struct aims for computational and memory efficiency. Implemented naively, dynamic programming algorithms in Python are prohibitively slow. As such Torch-Struct provides key primitives to help batch and vectorize these algorithms to take advantage of GPU computation and to minimize the overhead of backpropagating through chart-based dynamic programmming.
What general-purpose optimizations are included?

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. 
What non-annotated datasets are considered?

System description ::: Architecture The components involved in the process are: QnAMaker Portal: This is the Graphical User Interface (GUI) for using QnAMaker.  QnaMaker Management APIs: This is used for the extraction of Question-Answer (QA) pairs from semi-structured content.  Azure Search Index: Stores the KB with questions and answers as indexable columns, thus acting as a retrieval layer. QnaMaker WebApp: Acts as a layer between the Bot, Management APIs, and Azure Search Index.  Bot: Calls the WebApp with the User's query to get results.

What components is the QnAMaker composed of?