Conversational implicatures in English dialogue: Annotated dataset
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Abstract (arXiv)
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.
Key facts. Cited by 7 papers in the map (data through 14 Oct 2026). First posted on arXiv 25 Nov 2019.
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Citing papers per year: 2024: 1; 2025: 4; 2026: 2.
7 papers.
- 2025Pragmatics in the Era of Large Language Models: A Survey on Datasets, Evaluation, Opportunities and Challengescited by 31
- 2025Entailed Between the Lines: Incorporating Implication into NLIcited by 9
- 2024ImpScore: A Learnable Metric For Quantifying The Implicitness Level of Sentencecited by 3
- 2025The Pragmatic Mind of Machines: Tracing the Emergence of Pragmatic Competence in Large Language Modelscited by 3
- 2025Implicature in Interaction: Understanding Implicature Improves Alignment in Human-LLM Interactioncited by 0
- 2026DRInQ: Evaluating Conversational Implicature with Controlled Context Variationcited by 0
- 2026Evaluating Communicative Belief Updates in Large Language Models via Implicature Recognition and Cancellationcited by 0
- 2026Evaluating Communicative Belief Updates in Large Language Models via Implicature Recognition and Cancellation27 Jul 2026
- 2026DRInQ: Evaluating Conversational Implicature with Controlled Context Variation22 May 2026
- 2025Implicature in Interaction: Understanding Implicature Improves Alignment in Human-LLM Interaction29 Oct 2025
- 2025The Pragmatic Mind of Machines: Tracing the Emergence of Pragmatic Competence in Large Language Models24 May 2025
- 2025Pragmatics in the Era of Large Language Models: A Survey on Datasets, Evaluation, Opportunities and Challenges17 Feb 2025
- 2025Entailed Between the Lines: Incorporating Implication into NLI13 Jan 2025
- 2024ImpScore: A Learnable Metric For Quantifying The Implicitness Level of Sentence7 Nov 2024