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 annotation purposes, we created snippets of conversations as the ones shown in Example 1 and Example 2 consisting of the parent of the suspected trolling event, the suspected trolling event comment, and all of the direct responses to the suspected trolling event. However, the trade off is that snippets like this allow us to make use of Amazon Mechanical Turk (AMT) to have the dataset annotated, because it is not a big burden for a “turker” to work on an individual snippet in exchange for a small pay, and expedites the annotation process by distributing it over dozens of people. Specifically, for each snippet, we requested three annotators to label the four aspects previously described.
[EX A]: how was annotation done?

[EX Q]: 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.
[EX A]: How does the model work if no treebank is available?

[EX Q]: The embedding-wise convolution is to apply a convolution filter w $\in \mathbb {R}^{\mathcal {\\}k*d}$ to a window of $k$ word embeddings to generate a new feature, i.e., summarizing a local context of $k$ words. By applying the convolutional filter to all possible windows in the sentence, a feature map $c$ will be generated.
[EX A]:
What supports the claim that injected CNN into recurent units will enhance ability of the model to catch local context and reduce ambiguities?