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.

Input: Consider Input: To model an answer which is a collection of spans, the multi-span head uses the $\mathtt {BIO}$ tagging format BIBREF8: $\mathtt {B}$ is used to mark the beginning of a span, $\mathtt {I}$ is used to mark the inside of a span and $\mathtt {O}$ is used to mark tokens not included in a span. In this way, we get a sequence of chunks that can be decoded to a final answer - a collection of spans. 
 Question: How they use sequence tagging to answer multi-span questions?

Output: To model an answer which is a collection of spans, the multi-span head uses the $\mathtt {BIO}$ tagging format BIBREF8: $\mathtt {B}$ is used to mark the beginning of a span, $\mathtt {I}$ is used to mark the inside of a span and $\mathtt {O}$ is used to mark tokens not included in a span


Input: Consider 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?

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.


Input: Consider Input: To compare our neural models with the traditional approaches, we experimented with a number of existing models including: Support Vector Machine (SVM), a discriminative max-margin model; Logistic Regression (LR), a discriminative probabilistic model; and Random Forest (RF), an ensemble model of decision trees. 
 Question: what was their baseline comparison?


Output: Support Vector Machine (SVM) Logistic Regression (LR) Random Forest (RF)


