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.

[Q]: The datasets used for training, validation and testing contain sentences extracted from the Europarl corpus BIBREF1 and SoNaR corpus BIBREF2. The Europarl corpus is an open-source parallel corpus containing proceedings of the European Parliament. The Dutch section consists of 2,333,816 sentences and 53,487,257 words. The SoNaR corpus comprises two corpora: SONAR500 and SONAR1. The SONAR500 corpus consists of more than 500 million words obtained from different domains.
Question: What are the sizes of both datasets?
[A]: The Dutch section consists of 2,333,816 sentences and 53,487,257 words. The SONAR500 corpus consists of more than 500 million words obtained from different domains.

[Q]: We use OpenNMT BIBREF24 as the implementation of the NMT system for all experiments BIBREF5 . PBMT-R is a phrase-based method with a reranking post-processing step BIBREF18 . Hybrid performs sentence splitting and deletion operations based on discourse representation structures, and then simplifies sentences with PBMT-R BIBREF25 . SBMT-SARI BIBREF19 is syntax-based translation model using PPDB paraphrase database BIBREF26 and modifies tuning function (using SARI). Dress is an encoder-decoder model coupled with a deep reinforcement learning framework, and the parameters are chosen according to the original paper BIBREF20 .
Question: what state of the art methods did they compare with?
[A]: OpenNMT PBMT-R Hybrid SBMT-SARI Dress

[Q]: We improve over the previous state-of-the-art BIBREF35 for VQA dataset by around 6% in BLEU score and 10% in METEOR score. In the VQG-COCO dataset, we improve over BIBREF5 by 3.7% and BIBREF36 by 3.5% in terms of METEOR scores.
Question: What were the previous state of the art benchmarks?
[A]:
BIBREF35 for VQA dataset BIBREF5 BIBREF36