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.

Ex Input:
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?

Ex Output:
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


Ex Input:
We train a single layer LSTM with a 150-dimensional hidden state for hate / not hate classification. The input dimensionality is set to 100 and GloVe BIBREF26 embeddings are used as word input representations. 
 Question: What unimodal detection models were used?

Ex Output:
 single layer LSTM with a 150-dimensional hidden state for hate / not hate classification


Ex Input:
Manual verification of Twitter profiles: We verified each profile discovered manually by examining the profile picture, profile background image, recent tweets, and recent pictures posted by a user. 
 Question: How is gang membership verified?

Ex Output:

Manual verification