TASK DEFINITION: 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.
PROBLEM: 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?

SOLUTION: Yes

PROBLEM: Stanford - Twitter Sentiment Corpus (STS Corpus): STS Corpus contains 1,600K training tweets collected by a crawler from BIBREF0 . BIBREF0 constructed a test set manually with 177 negative and 182 positive tweets. The Stanford test set is small. However, it has been widely used in different evaluation tasks BIBREF0 BIBREF5 BIBREF13 .

Sanders - Twitter Sentiment Corpus: This dataset consists of hand-classified tweets collected by using search terms: INLINEFORM0 , #google, #microsoft and #twitter. We construct the dataset as BIBREF14 for binary classification.

Health Care Reform (HCR): This dataset was constructed by crawling tweets containing the hashtag #hcr BIBREF15 . Task is to predict positive/negative tweets BIBREF14 .
Question: Which datasets did they use?

SOLUTION: Stanford - Twitter Sentiment Corpus (STS Corpus) Sanders - Twitter Sentiment Corpus Health Care Reform (HCR)

PROBLEM: Our training data consists of 2.09M sentence pairs extracted from LDC corpus.
Question: Where does the vocabulary come from?

SOLUTION:
LDC corpus