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.

Example Input: mcdonald:11 established that, when no treebank annotations are available in the target language, training on multiple source languages outperforms training on one (i.e., multi-source model transfer outperforms single-source model transfer). Following guo:16, for each target language, we train the parser on six other languages in the Google universal dependency treebanks version 2.0 (de, en, es, fr, it, pt, sv, excluding whichever is the target language), and we use gold coarse POS tags.
Example Output: How does the model work if no treebank is available?

Example Input: A maximum of three contributors will listen to any audio clip. If an $<$audio,transcript$>$ pair first receives two up-votes, then the clip is marked as valid. If instead the clip first receives two down-votes, then it is marked as invalid. A contributor may switch between recording and validation as they wish. Only clips marked as valid are included in the official training, development, and testing sets for each language. Clips which did not recieve enough votes to be validated or invalidated by the time of release are released as "other". The train, test, and development sets are bucketed such that any given speaker may appear in only one. This ensures that contributors seen at train time are not seen at test time, which would skew results. Additionally, repetitions of text sentences are removed from the train, test, and development sets of the corpus.
Example Output: How is validation of the data performed?

Example Input: For the other techniques, we extract paragraphs containing any word from a predetermined list of LGTBQ terms (shown in Table TABREF19)
Example Output:
How do they identify discussions of LGBTQ people in the New York Times?