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: 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?

Answer: 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.


Question: First, we can observe that the final model “Ours with Mask and Ordered Triplets” outperforms the Baseline and Ablation models on all metrics in previously seen environments. The difference in performance is particularly evident for the Exact Match and Goal Match metrics, with our model increasing accuracy by 35% and 25% in comparison to the Baseline and Ablation models, respectively. These results suggest that providing the behavioral navigation graph to the model and allowing it to process this information as a knowledge base in an end-to-end fashion is beneficial. Lastly, it is worth noting that our proposed model (last row of Table TABREF28 ) outperforms all other models in previously seen environments. In particular, we obtain over INLINEFORM0 increase in EM and GM between our model and the next best two models. 
 Question: By how much did their model outperform the baseline?

Answer: increasing accuracy by 35% and 25% in comparison to the Baseline and Ablation models, respectively over INLINEFORM0 increase in EM and GM between our model and the next best two models


Question: We evaluate LinkNBed and baselines on two real world knowledge graphs: D-IMDB (derived from large scale IMDB data snapshot) and D-FB (derived from large scale Freebase data snapshot). 
 Question: On what data is the model evaluated?

Answer:
D-IMDB (derived from large scale IMDB data snapshot) D-FB (derived from large scale Freebase data snapshot)