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 evaluate the effectiveness of our LAN model on two typical knowledge graph completion tasks, i.e., link prediction and triplet classification.
[A]: Which knowledge graph completion tasks do they experiment with?

[Q]: For testing, we also applied the TongueNet on the UBC database BIBREF14 without any training to see the generalization ability of the model.
[A]: What previously annotated databases are available?

[Q]: We compare our approaches with related approaches of pivoting, multilingual NMT (MNMT) BIBREF19, and cross-lingual transfer without pretraining BIBREF16. Although it is challenging for one model to translate all zero-shot directions between multiple distant language pairs of MultiUN, MLM+BRLM-SA still achieves better performances on Es $
ightarrow $ Ar and Es $
ightarrow $ Ru than strong pivoting$_{
m m}$, which uses MNMT to translate source to pivot then to target in two separate steps with each step receiving supervised signal of parallel corpora.
[A]:
what are the pivot-based baselines?