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: For our classification experiments, we use the MH17 Twitter dataset introduced by BIBREF4, a dataset collected in order to study the flow of (dis)information about the MH17 plane crash on Twitter.

Answer: What dataset is used for this study?


Question: 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.

Answer: What general-purpose optimizations are included?


Question: By choosing English (En) as the pivot language, we perform pivot alignments for identical English segments on Europarl Fr-En and En-De parallel corpora BIBREF18 , constructing a multi-parallel corpus of Fr-En-De. Then each of the Fr*-De and Fr-De* pseudo parallel corpora is established from the multi-parallel data by applying the pivot language-based translation described in the previous section. For automatic translation, we utilize a pre-trained and publicly released NMT model for En $\rightarrow $ De and train another NMT model for En $\rightarrow $ Fr using the WMT'15 En-Fr parallel corpus BIBREF19 .

Answer:
Where do they collect the synthetic data?