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]: 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. 
[EX A]: Which neural architecture do they use as a base for their attention conflict mechanisms?

[EX Q]: Due to the high cost of pre-training BERT language model, we directly adopt parameters pre-trained by Google in Chinese general corpus. The named entity recognition is applied on both pathology report texts and query texts.
[EX A]: What data is the language model pretrained on?

[EX Q]: To increase the variety of corpus, we selected 4 English corpora and 4 Mandarin corpora in addition to the low resource language corpora.
[EX A]:
Do they test their approach on large-resource tasks?