instruction:
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:
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 use Waseem and Hovy BIBREF5, Davidson et al. BIBREF9, and Waseem et al. BIBREF10 as baselines and compare the results with our different fine-tuning strategies using pre-trained BERTbase model.  
 Question: What baseline is used?
answer:
Waseem and Hovy BIBREF5 Davidson et al. BIBREF9 Waseem et al. BIBREF10


question:
In this paper, in spite of previous statements, we present a system that uses rule-based and dictionary-based methods combined (in a way we prefer to call resource-based).  
 Question: What does their system consist of?
answer:

rule-based and dictionary-based methods