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: In this section, we describe our data collection for these 3 components. We first describe a series of pilot studies that we conducted in order to collect commonsense inference questions (Section SECREF4 ). In Section SECREF5 , we discuss the resulting data collection of questions, texts and answers via crowdsourcing on Amazon Mechanical Turk (henceforth MTurk). Section SECREF17 gives information about some necessary postprocessing steps and the dataset validation. 

Answer: how was the data collected?


Question: Even if LangID-High does not present a more accurate result, it does present a more compact one: LangID-High is 15.4 MB, while the combined wFST high resource models are 197.5 MB.

Answer: how is model compactness measured?


Question: Conclusion

Answer:
What is triangulation?