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.

[EX Q]: For testing, we also applied the TongueNet on the UBC database BIBREF14 without any training to see the generalization ability of the model.
[EX A]: What previously annotated databases are available?

[EX Q]: 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.
[EX A]: What general-purpose optimizations are included?

[EX Q]:  In this work, we consider words that can indicate the characteristics of the neighbor words as contextual keywords and develop an approach to generate features from the automatically extracted contextual keywords.
[EX A]:
What contextual features are used?