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.

Q: 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?

A: 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.
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Q: Next, for emergent language, we show that the MASC architecture can achieve very high localization accuracy, significantly outperforming the baseline that does not include this mechanism. 
 Question: What evaluation metrics did the authors look at?

A: localization accuracy
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Q: As shown in Table TABREF39 , the proposed framework consistently obtains the best scores on all of the four datasets. Compared with the winning systems of SemEval ABSA, our framework achieves 5.0%, 1.6%, 1.4%, 1.3% absolute gains on INLINEFORM0 , INLINEFORM1 , INLINEFORM2 and INLINEFORM3 respectively.

Our framework can outperform RNCRF, a state-of-the-art model based on dependency parsing, on all datasets. We also notice that RNCRF does not perform well on INLINEFORM0 and INLINEFORM1 (3.7% and 3.9% inferior than ours). We find that INLINEFORM2 and INLINEFORM3 contain many informal reviews, thus RNCRF's performance degradation is probably due to the errors from the dependency parser when processing such informal texts. 
 Question: By how much do they outperform state-of-the-art methods?

A:
Compared with the winning systems of SemEval ABSA, our framework achieves 5.0%, 1.6%, 1.4%, 1.3% absolute gains on INLINEFORM0 , INLINEFORM1 , INLINEFORM2 and INLINEFORM3 respectively.
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