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: Results in Table TABREF13 show ten probes. Again, we see the performance of baseline ELMo-transformer and mSynC are similar, with mSynC doing slightly worse on 7 out of 9 tasks. 
 Question: For how many probe tasks the shallow-syntax-aware contextual embedding perform better than ELMo’s embedding?

A: performance of baseline ELMo-transformer and mSynC are similar, with mSynC doing slightly worse on 7 out of 9 tasks
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Q: 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?

A: 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
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Q: Our baseline decoder is a standard beam search decoder BIBREF5 with several straightforward performance optimizations 
 Question: What baseline decoder do they use?

A:
a standard beam search decoder BIBREF5 with several straightforward performance optimizations
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