Conversational implicatures in English dialogue: Annotated dataset
This addresses the challenge of improving human-computer interaction for natural language processing applications, but it is incremental as it focuses on dataset creation rather than a new method.
The paper tackles the problem of machines failing to understand conversational implicatures in dialogue by introducing an annotated dataset of English dialogue snippets with context, utterance, and implicated meanings, collected from TOEFL tests and movie scripts and manually annotated.
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.