Definition: In this task, you will be presented with a context from an academic paper and you have to write an answerable question based on the context. Your questions can be extractive, abstractive, or yes-no questions.

Positive Example 1 -
Input: Questions are gathered from anonymized, aggregated queries to the Google search engine. Queries that are likely to be yes/no questions are heuristically identified: we found selecting queries where the first word is in a manually constructed set of indicator words and are of sufficient length, to be effective.

Questions are only kept if a Wikipedia page is returned as one of the first five results, in which case the question and Wikipedia page are given to a human annotator for further processing.

Annotators label question/article pairs in a three-step process. First, they decide if the question is good, meaning it is comprehensible, unambiguous, and requesting factual information. This judgment is made before the annotator sees the Wikipedia page. Next, for good questions, annotators find a passage within the document that contains enough information to answer the question. Annotators can mark questions as “not answerable" if the Wikipedia article does not contain the requested information. Finally, annotators mark whether the question's answer is “yes" or “no". Annotating data in this manner is quite expensive since annotators need to search entire Wikipedia documents for relevant evidence and read the text carefully.
Output: how was the dataset built?

Positive Example 2 -
Input: Using this annotation model, we create a new large publicly available dataset of English tweets.
Output: In what language are the tweets?

Negative Example 1 -
Input: Using this annotation model, we create a new large publicly available dataset of English tweets.
Output: In what language is tweets?

Negative Example 2 -
Input: Questions are gathered from anonymized, aggregated queries to the Google search engine. Queries that are likely to be yes/no questions are heuristically identified: we found selecting queries where the first word is in a manually constructed set of indicator words and are of sufficient length, to be effective.

Questions are only kept if a Wikipedia page is returned as one of the first five results, in which case the question and Wikipedia page are given to a human annotator for further processing.

Annotators label question/article pairs in a three-step process. First, they decide if the question is good, meaning it is comprehensible, unambiguous, and requesting factual information. This judgment is made before the annotator sees the Wikipedia page. Next, for good questions, annotators find a passage within the document that contains enough information to answer the question. Annotators can mark questions as “not answerable" if the Wikipedia article does not contain the requested information. Finally, annotators mark whether the question's answer is “yes" or “no". Annotating data in this manner is quite expensive since annotators need to search entire Wikipedia documents for relevant evidence and read the text carefully.
Output: What is the size of the dataset?

Now complete the following example -
Input: 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.
Output: 



Output: What datasets are used in training?