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:
Macaw also supports Wizard of Oz studies or intermediary-based information seeking studies. The architecture of Macaw for such setup is presented in FIGREF16. As shown in the figure, the seeker interacts with a real conversational interface that supports multi-modal and mixed-initiative interactions in multiple devices. The intermediary (or the wizard) receives the seeker's message and performs different information seeking actions with Macaw. All seeker-intermediary and intermediary-system interactions will be logged for further analysis. 
 Question: What is a wizard of oz setup?
answer:
seeker interacts with a real conversational interface intermediary (or the wizard) receives the seeker's message and performs different information seeking actions


question:
To capture this interesting property, we propose a new tagging scheme consisting of three tags, namely { INLINEFORM0 }.

INLINEFORM0 tag indicates that the current word appears before the pun in the given context.

INLINEFORM0 tag highlights the current word is a pun.

INLINEFORM0 tag indicates that the current word appears after the pun. 
 Question: What is the tagging scheme employed?
answer:
A new tagging scheme that tags the words before and after the pun as well as the pun words.


question:
is the standard word counting method whereby the feature vector represents the term frequency of the words in a sentence. is similar to BoW, except that it is derived by the counting of the words in the sentence weighted by individual word's term-frequency and inverse-document-frequency BIBREF31 . is derived from clustering of words-embeddings with k-means into 5000 clusters, and follow by BoW representation of the words in 5000 clusters. 
 Question: What other non-neural baselines do the authors compare to? 
answer:

bag of words, tf-idf, bag-of-means