The Pragmatics of Indirect Commands in Collaborative Discourse
Today's artificial assistants are typically prompted to perform tasks through direct, imperative commands such as \emph{Set a timer} or \emph{Pick up the box}. However, to progress toward more natural exchanges between humans and these assistants, it is important to understand the way non-imperative utterances can indirectly elicit action of an addressee. In this paper, we investigate command types in the setting of a grounded, collaborative game. We focus on a less understood family of utterances for eliciting agent action, locatives like \emph{The chair is in the other room}, and demonstrate how these utterances indirectly command in specific game state contexts. Our work shows that models with domain-specific grounding can effectively realize the pragmatic reasoning that is necessary for more robust natural language interaction.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Towards an Analysis of Discourse and Interactional Pragmatic Reasoning Capabilities of Large Language Models
In this work, we want to give an overview on which pragmatic abilities have been tested in LLMs so far and how these tests have been carried out. To do this, we first discuss the scope of the field of pragmatics and sugg…
Modelling the Interpretation of Discourse Connectives by Bayesian Pragmatics
Discourse over Discourse: The Need for an Expanded Pragmatic Focus in Conversational AI
The summarization of conversation, that is, discourse over discourse, elevates pragmatic considerations as a pervasive limitation of both summarization and other applications of contemporary conversational AI. Building o…
Conversation SummarizationDependent Types for Pragmatics
This paper proposes the use of dependent types for pragmatic phenomena such as pronoun binding and presupposition resolution as a type-theoretic alternative to formalisms such as Discourse Representation Theory and Dynam…
Pragmatic competence of pre-trained language models through the lens of discourse connectives
As pre-trained language models (LMs) continue to dominate NLP, it is increasingly important that we understand the depth of language capabilities in these models. In this paper, we target pre-trained LMs' competence in p…
Implicatures