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:
This kind of error affects the precision and recall for the “troll” and “engage” classes. A solution to this problem may be the inclusion of longer parts of the conversation.  
 Question: What potential solutions are suggested?

Ex Output:
 inclusion of longer parts of the conversation


Ex Input:
The dataset consists of a set of polysemous words: 20 nouns, 20 verbs, and 10 adjectives and specifies 20 to 100 contexts per word, with the total of 4,664 contexts, drawn from the Open American National Corpus. Given a set of contexts of a polysemous word, the participants of the competition had to divide them into clusters by sense of the word. The contexts are manually labelled with WordNet senses of the target words, the gold standard clustering is generated from this labelling.

The task allows two setups: graded WSI where participants can submit multiple senses per word and provide the probability of each sense in a particular context, and non-graded WSI where a model determines a single sense for a word in context. In our experiments we performed non-graded WSI. We considered the most suitable sense as the one with the highest cosine similarity with embeddings of the context, as described in Section SECREF9. 
 Question: How are the different senses annotated/labeled? 

Ex Output:
The contexts are manually labelled with WordNet senses of the target words


Ex Input:
We collect a novel dataset of 230K+ recipe texts and 1M+ user interactions (reviews) over 18 years (2000-2018) from Food.com. 
 Question: Where do they get the recipes from?

Ex Output:

from Food.com