instruction:
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.
question:
In particular, we aggregate documents from the CommonCrawl dataset that has the most overlapping n-grams with the questions. We name this dataset STORIES since most of the constituent documents take the form of a story with long chain of coherent events. Figure 5 -left and middle show that STORIES always yield the highest accuracy for both types of input processing.
Question: Which of their training domains improves performance the most?
answer:
documents from the CommonCrawl dataset that has the most overlapping n-grams with the question

question:
We then show the tweet text and image and we ask them to classify it in one of 6 categories: No attacks to any community, racist, sexist, homophobic, religion based attacks or attacks to other communities.
Question: What annotations are available in the dataset - tweat used hate speach or not?
answer:
No attacks to any community racist sexist homophobic religion based attacks attacks to other communities

question:
We used the Reuters-8 dataset without stop words from BIBREF27 aiming at single-label classification, which is a preprocessed format of the Reuters-21578.
Question: Which dataset has been used in this work?
answer:
Reuters-8 dataset without stop words