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.

Input: Consider Input: The ensembles were formed by simply averaging the predictions from the constituent single models. 
 Question: How does their ensemble method work?

Output: simply averaging the predictions from the constituent single models


Input: Consider Input: Similarly the agents utterances can be clustered to identify system responses. However, we argue that rather than treating user utterances and agents responses in an isolated manner, there is merit in jointly clustering them. There is adjacency information of these utterances that can be utilized to identify better user intents and system responses. 
 Question: Do they study frequent user responses to help automate modelling of those?

Output: Yes


Input: Consider Input: The model is sequential in nature, consisting of the following steps: read text, process it into a dynamic relational memory and then attention conditioned on the question generates the answer. We model the dynamic memory in a fashion similar to Recurrent Entity Networks BIBREF17 and then equip it with an additional relational memory. 
 Question: What is the architecture of the neural network?


Output: extends memory-augmented neural networks with a relational memory to reason about relationships between multiple entities present within the text.  The model is sequential in nature, consisting of the following steps: read text, process it into a dynamic relational memory and then attention conditioned on the question generates the answer. We model the dynamic memory in a fashion similar to Recurrent Entity Networks BIBREF17 and then equip it with an additional relational memory.