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]: Table TABREF24 shows that while the acceleration of trading decoder layers for encoding layers in training is small, in decoding is significant. Specifically, the Transformer with 10 encoder layers and 2 decoder layers is $2.32$ times as fast as the 6-layer Transformer while achieving a slightly higher BLEU.
[A]: How much is decoding speed increased by increasing encoder and decreasing decoder depth?


[Q]: We use the Dialogue State Tracking Competition 2 (DSTC2) dataset BIBREF27 which is the most widely used dataset for research on task-oriented chatbots.  We also used two other datasets recently open-sourced by Google Research BIBREF28 which are M2M-sim-M (dataset in movie domain) and M2M-sim-R (dataset in restaurant domain). 
[A]: What are the three datasets used?


[Q]: Notably, this increase is observed after the conclusion of the US presidential primaries and during the period of the Democratic and Republican National Conventions and does not reduce even after the conclusion of the US presidential elections held on November 8.
[A]:
What other political events are included in the database?