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]: Modern Standard Arabic (MSA) and Classical Arabic (CA) have two types of vowels, namely long vowels, which are explicitly written, and short vowels, aka diacritics, which are typically omitted in writing but are reintroduced by readers to properly pronounce words. We show that our model achieves a case ending error rate (CEER) of 3.7% for MSA and 2.5% for CA. For MSA, this CEER is more than 60% lower than other state-of-the-art systems such as Farasa and the RDI diacritizer, which are trained on the same dataset and achieve CEERs of 10.7% and 14.4% respectively.
Question: what are the previous state of the art?
[EX A]: Farasa RDI

[EX Q]: To test the difficulty of our dataset, we checked the majority class label and the accuracies of five state-of-the-art NLI models adopting different approaches: BiMPM (Bilateral Multi-Perspective Matching Model; BIBREF31 , BIBREF31 ), ESIM (Enhanced Sequential Inference Model; BIBREF32 , BIBREF32 ), Decomposable Attention Model BIBREF33 , KIM (Knowledge-based Inference Model; BIBREF34 , BIBREF34 ), and BERT (Bidirectional Encoder Representations from Transformers model; BIBREF35 , BIBREF35 ). Regarding BERT, we checked the performance of a model pretrained on Wikipedia and BookCorpus for language modeling and trained with SNLI and MultiNLI.
Question: What NLI models do they analyze?
[EX A]: BiMPM ESIM Decomposable Attention Model KIM BERT

[EX Q]: We formalize this intuition in a model called NextSum, which selects the next summary sentence based not only on properties of the source text, but also on the previously selected sentences in the summary.
Question: How does nextsum work?
[EX A]:
selects the next summary sentence based not only on properties of the source text, but also on the previously selected sentences in the summary