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:
Our attempt at language pre-training fell short of our expectations in all but one tested dataset. Our pre-training was unsuccessful in improving accuracy, even when applied to networks larger than those reported. 
 Question: Does pre-training on general text corpus improve performance?

Ex Output:
No


Ex Input:
 For the embeddings, we relied on $AraVec$ BIBREF30 for Arabic, FastText BIBREF31 for French, and Word2vec Google News BIBREF32 for English .  
 Question: What monolingual word representations are used?

Ex Output:
AraVec for Arabic, FastText for French, and Word2vec Google News for English.


Ex Input:
In our final system, after pre-training the forward and backward LMs separately, we remove the top layer softmax and concatenate the forward and backward LM embeddings to form bidirectional LM embeddings, i.e., INLINEFORM0 .  
 Question: how are the bidirectional lms obtained?

Ex Output:

They pre-train forward and backward LMs separately, remove top layer softmax, and concatenate to obtain the bidirectional LMs.