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.

[Q]: We then proceed to connect these mentions i) if they co-occur within the same document (we will refer to this as DOC-BASED edges), ii) if the pair of named entity mentions is identical (MATCH edges—these may connect nodes across and within documents), or iii) if they are in the same coreference chain, as predicted by the external coreference system (COREF edges).
[A]: Did they use a relation extraction method to construct the edges in the graph?


[Q]: Our model BiLSTM+CNN(grapheme-level) and BiLSTM+CNN(G)+POS outperforms all other model experimented in OurNepali and ILPRL dataset respectively.
[A]: What is the best model?


[Q]: We evaluated the phrase compositionality models on the adjective–noun and noun–noun phrase similarity tasks compiled by Mitchell2010, using the same evaluation scheme as in the original work. Spearman's INLINEFORM0 between phrasal similarities derived from our compositional functions and the human annotators (computed individually per annotator and then averaged across all annotators) was the evaluation measure.
[A]:
How do they score phrasal compositionality?