  Human dialogue often contains utterances having meanings entirely different
from the sentences used and are clearly understood by the interlocutors. But in
human-computer interactions, the machine fails to understand the implicated
meaning unless it is trained with a dataset containing the implicated meaning
of an utterance along with the utterance and the context in which it is
uttered. In linguistic terms, conversational implicatures are the meanings of
the speaker's utterance that are not part of what is explicitly said. In this
paper, we introduce a dataset of dialogue snippets with three constituents,
which are the context, the utterance, and the implicated meanings. These
implicated meanings are the conversational implicatures. The utterances are
collected by transcribing from listening comprehension sections of English
tests like TOEFL (Test of English as a Foreign Language) as well as scraping
dialogues from movie scripts available on IMSDb (Internet Movie Script
Database). The utterances are manually annotated with implicatures.
