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]: To simplify the task, we set up a binary classification: candidates who have been liked or shortlisted are considered part of the hirable class and others part of the not hirable class. 
 Question: How is "hirability" defined?
[A]: candidates who have been liked or shortlisted are considered part of the hirable class


[Q]: We notice that the concatenation consistently outperforms the gated-attention mechanism for both training and testing instructions. We suspect that the gated-attention is useful in the scenarios where objects are described in terms of multiple attributes, but it has no to harming effect when it comes to the order connectors. 
 Question: Why they conclude that the usage of Gated-Attention provides no competitive advantage against concatenation in this setting?
[A]: concatenation consistently outperforms the gated-attention mechanism for both training and testing instructions


[Q]: The first model, referred as `LSTM (PTB)' in the following sections, was trained on the sentences from Penn Treebank BIBREF12. The second model, referred as `LSTM (FTB)', was trained on the sentences from French Treebank BIBREF13. We incorporate two pretrained English language models: one trained on the Billion Word benchmark (referred as `LSTM (1B)') from BIBREF14, and the other trained on English Wikipedia (referred as `LSTM (enWiki)') from BIBREF3. For French, we trained a large LSTM language model (referred as `LSTM (frWaC)') on a random subset (about 4 million sentences, 138 million word tokens) of the frWaC dataset BIBREF15. 
 Question: What is the size of the datasets employed?
[A]:
(about 4 million sentences, 138 million word tokens) one trained on the Billion Word benchmark