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]: 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?
[A]: Yes


[Q]: In this paper, we present an extraction-then-synthesis framework for machine reading comprehension shown in Figure 1 , in which the answer is synthesized from the extraction results. We build an evidence extraction model to predict the most important sub-spans from the passages as evidence, and then develop an answer synthesis model which takes the evidence as additional features along with the question and passage to further elaborate the final answers. 
 Question: Which framework they propose in this paper?
[A]:  extraction-then-synthesis framework


[Q]: For the McGurk effect, we attempt an illusion for a language token (e.g. phoneme, word, sentence) $x$ by creating a video where an audio stream of $x$ is visually dubbed over by a person saying $x^{\prime }\ne x$ . The illusion $f(x^{\prime },x)$ affects a listener if they perceive what is being said to be $y\ne x$ if they watched the illusory video whereas they perceive $x$ if they had either listened to the audio stream without watching the video or had watched the original unaltered video, depending on specification.  A prototypical example is that the audio of the phoneme “baa,” accompanied by a video of someone mouthing “vaa”, can be perceived as “vaa” or “gaa” (Figure 1 ). 
 Question: What is the McGurk effect?
[A]:
a perceptual illusion, where listening to a speech sound while watching a mouth pronounce a different sound changes how the audio is heard