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 Input:
For MSA, we acquired the diacritized corpus that was used to train the RDI BIBREF7 diacritizer and the Farasa diacritizer BIBREF31. The corpus contains 9.7M tokens with approximately 194K unique surface forms (excluding numbers and punctuation marks). For testing, we used the freely available WikiNews test set BIBREF31, which is composed of 70 MSA WikiNews articles (18,300 tokens) and evenly covers a variety of genres including politics, economics, health, science and technology, sports, arts and culture. For CA, we obtained a large collection of fully diacritized classical texts (2.7M tokens) from a book publisher, and we held-out a small subset of 5,000 sentences (approximately 400k words) for testing. 
 Question: what datasets were used?

Ex Output:
diacritized corpus that was used to train the RDI BIBREF7 diacritizer and the Farasa diacritizer BIBREF31 WikiNews test set BIBREF31  large collection of fully diacritized classical texts (2.7M tokens) from a book publisher


Ex Input:
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?

Ex Output:
OpenNMT PBMT-R Hybrid SBMT-SARI Dress


Ex Input:
Despite this, JESSI performs second on Subtask A with an F1 score of 77.78% among 33 other team submissions. It also performs well on Subtask B with an F1 score of 79.59%. 
 Question: How did they do compared to other teams?

Ex Output:

second on Subtask A with an F1 score of 77.78% among 33 other team submissions performs well on Subtask B with an F1 score of 79.59%