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:
A simple, yet recurrent text model is the well-known word co-occurrence network. After optional textual pre-processing steps, in a co-occurrence network each different word becomes a node and edges are established via co-occurrence in a desired window. A common strategy connects only adjacent words in the so called word adjacency networks.

While the co-occurrence representation yields good results in classification scenarios, some important features are not considered in the model. For example, long-range syntactical links, though less frequent than adjacent syntactical relationships, might be disregarded from a simple word adjacency approach BIBREF12. 
 Question: What is the reason that traditional co-occurrence networks fail in establishing links between similar words whenever they appear distant in the text?

Ex Output:
long-range syntactical links, though less frequent than adjacent syntactical relationships, might be disregarded from a simple word adjacency approach


Ex Input:
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 Output:
BiMPM ESIM Decomposable Attention Model KIM BERT


Ex Input:
To create our training dataset, we followed an approach similar to LASER. The dataset contains 6 languages: English, Spanish, German, Dutch, Korean and Chinese Mandarin. The dataset was created by using translations provided by Tatoeba and OpenSubtitles BIBREF16. 
 Question: Which corpus do they use?

Ex Output:

The dataset was created by using translations provided by Tatoeba and OpenSubtitles BIBREF16.