TASK DEFINITION: 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.
PROBLEM: The annotation projection method, which we follow in this work, is one way to address this problem. It was introduced for POS tagging, base noun phrase bracketing, NER tagging, and inflectional morphological analysis BIBREF29 but it has also been used for dependency parsing BIBREF30 , role labeling BIBREF31 , BIBREF32 and semantic parsing BIBREF26 .

SOLUTION: Do the authors test their annotation projection techniques on tasks other than AMR?

PROBLEM: We create two models both of which constitutes of three main parts: encoder, interaction and classifier and take two sequences as input. The encoder is shared among the sequences simply uses two stacked GRU layers. The interaction part consists of only attention for one model while for the another one it consists of attention and conflict combined as shown in (eqn.11) . The classifier part is simply stacked fully-connected layers. 

SOLUTION: Which neural architecture do they use as a base for their attention conflict mechanisms?

PROBLEM: During the final training process, 500 validation data is used to generate the recurrent neural model, which is 3% of the training data. After finishing the training, the accuracy of the generated model using validation data from the source corpus was 74.40%

SOLUTION:
What dataset is used to measure accuracy?