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.

Table TABREF46 shows that our Open model achieves more than 3 points of f1-score than the state-of-the-art result, and RelAwe with DepPath&RelPath achieves the best in both Closed and Open settings.
How big is improvement over the old  state-of-the-art performance on CoNLL-2009 dataset?

Our ASR and ST models follow the architecture in berard2018end, but have 3 decoder layers like that in pino2019harnessing.
What is the architecture of their model?

Approach ::: Method: Training and Testing ::: Experiment 1: Representation
Some of the problems encountered by prior approaches seem to be attributable to the use of infix notation. In this experiment, we compare translation BLEU-2 scores to spot the differences in representation interpretability. Approach ::: Method: Training and Testing ::: Experiment 2: State-of-the-art
This experiment compares our networks to recent previous work. We count a given test score by a simple “correct versus incorrect" method. The answer to an expression directly ties to all of the translation terms being correct, which is why we do not consider partial precision. We compare average accuracies over 3 test trials on different randomly sampled test sets from each MWP dataset.

How is this problem evaluated?