In this task, you will be presented with a context from an academic paper and a question separated with a
. You have to answer the question based on the context.

[EX Q]: Our data set contains $3,209$ reviews about 553 different cars from 49 different car manufacturers.
Question: How big is dataset of car-speak language?
[EX A]: $3,209$ reviews

[EX Q]: Our model yields the best results on both UAS and LAS metrics of all languages except the Japanese. As for Japanese, our model gives unsatisfactory results because the original treebank was written in Roman phonetic characters instead of hiragana, which is used by both common Japanese writing and our pre-trained embeddings. Despite this, our model overall still gives 1.0% higher average UAS and LAS than the previous best parser, BIAF. Although both BIAF and STACKPTR parsers have achieved relatively high parsing accuracies on the 12 languages and have all UAS higher than 90%, our model achieves state-of-the-art results in all languages for both UAS and LAS. Overall, our model reports more than 1.0% higher average UAS than STACKPTR and 0.3% higher than BIAF.
Question: What are performance compared to former models?
[EX A]: model overall still gives 1.0% higher average UAS and LAS than the previous best parser, BIAF our model reports more than 1.0% higher average UAS than STACKPTR and 0.3% higher than BIAF

[EX Q]: We evaluate the generations with the ROUGE BIBREF29 and METEOR BIBREF30 metrics using the true sentences as targets.
Question: What metrics are used for evaluation?
[EX A]:
ROUGE BIBREF29 and METEOR BIBREF30