TASK DEFINITION: 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.
PROBLEM: This paper used the real-time method to randomly collect 10% of publicly available English tweets using several pre-defined DDEO-related queries (Table TABREF6 ) within a specific time frame.  
 Question: Do they evaluate only on English data?

SOLUTION: Yes

PROBLEM: In its general form, a type-logical grammar consists of following components: 
 Question: Does Grail accept Prolog inputs?

SOLUTION: No

PROBLEM: holistic: The baseline model which maps the holistic image feature and LSTM-encoded question feature to a common space and perform element-wise multiplication between them.

TraAtt: The traditional attention model, implementation of WTL model BIBREF9 using the same $3\times 3$ regions in SalAtt model.

RegAtt: The region attention model which employs our novel attention method, same as the SalAtt model but without region pre-selection.

ConAtt: The convolutional region pre-selection attention model which replaces the BiLSTM in SalAtt model with a weight-sharing linear mapping, implemented by a convolutional layer.

Besides, we also compare our SalAtt model with the popular baseline models i.e. iBOWIMG BIBREF4 , VQA BIBREF1 , and the state-of-the-art attention-based models i.e. WTL BIBREF9 , NMN BIBREF21 , SAN BIBREF14 , AMA BIBREF33 , FDA BIBREF34 , D-NMN BIBREF35 , DMN+ BIBREF8 on two tasks of COCO-VQA. 
 Question: To which previous papers does this work compare its results?

SOLUTION:
holistic TraAtt RegAtt ConAtt ConAtt iBOWIMG  VQA VQA WTL  NMN  SAN  AMA  FDA  D-NMN DMN+