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.

Example Input: A context is upward entailing (shown by [... $\leavevmode {\color {red!80!black}\uparrow }$ ]) that allows an inference from ( "Introduction" ) to ( "Introduction" ), where French dinner is replaced by a more general concept dinner.  On the other hand, a downward entailing context (shown by [... $\leavevmode {\color {blue!80!black}\downarrow }$ ]) allows an inference from ( "Introduction" ) to ( "Introduction" ), where workers is replaced by a more specific concept new workers. All [ workers $\leavevmode {\color {blue!80!black}\downarrow }$ ] [joined for a French dinner $\leavevmode {\color {red!80!black}\uparrow }$ ] All workers joined for a dinner All new workers joined for a French dinner Not all [new workers $\leavevmode {\color {red!80!black}\uparrow }$ ] joined for a dinner Not all workers joined for a dinner 
 Question: How do they define upward and downward reasoning?
Example Output: Upward reasoning is defined as going from one specific concept to a more general one. Downward reasoning is defined as the opposite, going from a general concept to one that is more specific.

Example Input: We crowdsourced multiple-choice questions in two parts, encouraging workers to be imaginative and varied in their use of language. First, workers were given a seed qualitative relation q+/-( INLINEFORM0 ) in the domain, expressed in English (e.g., “If a surface has more friction, then an object will travel slower”), and asked to enter two objects, people, or situations to compare. 
 Question: Do all questions in the dataset allow the answers to pick from 2 options?
Example Output: Yes

Example Input: The functional dissimilarity score was computed using sentences from the test set in CoNLL 2017 Universal Dependencies task BIBREF20 for the relevant languages with the provided UPOS sequences. We computed the nearest neighbours experiment for all languages in the training data for the above models. 
 Question: Which evaluation metrics do they use for language modelling?
Example Output:
 functional dissimilarity score nearest neighbours experiment