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.

The best baseline model is NO-MOVE, reaching an accuracy of 30.3% on single sentences and 0.3 on complete paragraphs. 
 Question: How well did the baseline perform?
accuracy of 30.3% on single sentences and 0.3 on complete paragraphs

Arab-Tweet. For modeling age and gender, we use Arap-Tweet BIBREF19 , which we will refer to as Arab-Tweet. UBC Twitter Gender Dataset. We also develop an in-house Twitter dataset for gender. We manually labeled 1,989 users from each of the 21 Arab countries. The dialect identification model in AraNet is based on our winning system in the MADAR shared task 2 BIBREF20 as described in BIBREF12. The corpus is divided into train, dev and test, and the organizers masked test set labels. We make use of two datasets, the LAMA-DINA dataset from BIBREF22, a Twitter dataset with a combination of gold labels from BIBREF23 and distant supervision labels. The rest of the dataset is only labeled using distant supervision (LAMA-DIST) ($182,605$ tweets) . For more information about the dataset, readers are referred to BIBREF22.  We use the dataset for irony identification on Arabic tweets released by IDAT@FIRE2019 shared-task BIBREF24. We collect 15 datasets related to sentiment analysis of Arabic, including MSA and dialects BIBREF25, BIBREF26, BIBREF27, BIBREF1, BIBREF28, BIBREF29, BIBREF30, BIBREF31, BIBREF32, BIBREF33, BIBREF34. Table TABREF28 shows all the corpora we use.  
 Question: What datasets are used in training?
Arap-Tweet BIBREF19  an in-house Twitter dataset for gender the MADAR shared task 2 BIBREF20 the LAMA-DINA dataset from BIBREF22 LAMA-DIST Arabic tweets released by IDAT@FIRE2019 shared-task BIBREF24 BIBREF25, BIBREF26, BIBREF27, BIBREF1, BIBREF28, BIBREF29, BIBREF30, BIBREF31, BIBREF32, BIBREF33, BIBREF34

More specifically, we explore three regularization terms to address the problem: (1) a regularization term associated with neutral features; (2) the maximum entropy of class distribution regularization term; and (3) the KL divergence between reference and predicted class distribution. 
 Question: What are the three regularization terms?

a regularization term associated with neutral features the maximum entropy of class distribution regularization term the KL divergence between reference and predicted class distribution