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:
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?
answer:
Yes

question:
Our method is purely text-based, and ignores the publication date and the source of the article. It combines task-specific embeddings, produced by a two-level attention-based deep neural network model, with manually crafted features (stylometric, lexical, grammatical, and semantic), into a kernel-based SVM classifier.
Question: what types of features were used?
answer:
stylometric, lexical, grammatical, and semantic

question:
In total around 500 different workers were involved in the annotation. about 500
Question: How many people participated in their evaluation study of table-to-text models?
answer:
about 500