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:
We evaluate our proposed model on a Twitter dataset obtained from the authors of BIBREF12. Our final dataset consists of 11,576 users (i.e, fact-checkers), 4,732 fact-checking URLs and 63,429 interactions. The dataset also contains each user's social network information. Note that each user's social relationship is restricted within available users in the dataset. 
 Question: What dataset is used?

Ex Output:
Twitter dataset obtained from the authors of BIBREF12


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:
Corpus Embedding
Suppose that INLINEFORM0 is the target low-resource corpus, we are interested in optimizing the acoustic model with a much larger training corpora set INLINEFORM1 where INLINEFORM2 is the number of corpora and INLINEFORM3 . Each corpus INLINEFORM4 is a collection of INLINEFORM5 pairs where INLINEFORM6 is the input features and INLINEFORM7 is its target.

Our purpose here is to compute the embedding INLINEFORM0 for each corpus INLINEFORM1 where INLINEFORM2 is expected to encode information about its corpus INLINEFORM3 . Those embeddings can be jointly trained with the standard multilingual model BIBREF4 . First, the embedding matrix INLINEFORM4 for all corpora is initialized, the INLINEFORM5 -th row of INLINEFORM6 is corresponding to the embedding INLINEFORM7 of the corpus INLINEFORM8 . Next, during the training phase, INLINEFORM9 can be used to bias the input feature INLINEFORM10 as follows. DISPLAYFORM0

where INLINEFORM0 is an utterance sampled randomly from INLINEFORM1 , INLINEFORM2 is its hidden features, INLINEFORM3 is the parameter of the acoustic model and Encoder is the stacked bidirectional LSTM as shown in Figure. FIGREF5 . Next, we apply the language specific softmax to compute logits INLINEFORM4 and optimize them with the CTC objective BIBREF29 . The embedding matrix INLINEFORM5 can be optimized together with the model during the training process. 
 Question: How do they compute corpus-level embeddings?

Ex Output:

First, the embedding matrix INLINEFORM4 for all corpora is initialized during the training phase, INLINEFORM9 can be used to bias the input feature Next, we apply the language specific softmax to compute logits INLINEFORM4 and optimize them with the CTC objective