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]: An acoustic word embedding is a function that takes as input a speech segment corresponding to a word, INLINEFORM0 , where each INLINEFORM1 is a vector of frame-level acoustic features, and outputs a fixed-dimensional vector representing the segment, INLINEFORM2 . 
 Question: How do they represent input features of their model to train embeddings?
[A]: a vector of frame-level acoustic features


[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?
[A]: Farasa RDI


[Q]: Our systems are attentional encoder-decoder networks BIBREF0 . We base our implementation on the dl4mt-tutorial, which we enhanced with new features such as ensemble decoding and pervasive dropout. 
 Question: what are the baseline systems?
[A]:
attentional encoder-decoder networks BIBREF0


