Conversational implicatures in English dialogue: Annotated dataset

TL;DR

This paper introduces an annotated dataset of English dialogue snippets containing conversational implicatures, aiming to improve machine understanding of implied meanings in human dialogue.

Contribution

It provides a novel, manually annotated dataset of dialogue with implicatures, combining test transcriptions and movie scripts for advancing implicature detection.

Findings

Dataset includes context, utterance, and implicature annotations

Manual annotation ensures high-quality implicature labels

Dataset covers diverse dialogue sources like TOEFL and movie scripts

Abstract

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.