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The Co-Constructed Logic Framework for Understanding Children’s Acts and Their Intentions
Author(s) -
Fan Hong-ya,
Zeshan Ren
Publication year - 2021
Publication title -
theory and practice in language studies
Language(s) - English
Resource type - Journals
eISSN - 2053-0692
pISSN - 1799-2591
DOI - 10.17507/tpls.1111.12
Subject(s) - computer science , coreference , ambiguity , abductive reasoning , metonymy , pragmatics , inference , artificial intelligence , construct (python library) , defeasible estate , metaphor , linguistics , natural language processing , cognitive science , resolution (logic) , psychology , philosophy , programming language
With the characteristics of the nonmonotonic logic and defeasible inference, abductive reasoning has been formalized in the field of artificial intelligence, dealing with the local pragmatics (e.g., the resolution of coreference, resolving syntactic and lexical ambiguity and interpreting metonymy and metaphor), recognizing discourse structure and even the speaker’s plan and other issues for natural language understanding. However, Hobbs’ analysis of abduction in recognizing the speaker’s plan was conducted only from the point of view of the verbal information processing that the listener does. To demonstrate the collaborative way that conversational partners working together to understand the logic of human acts and their intentions, this article analyzes the two conversations about the parents questioning their children’s intention for their acts with an abductive reasoning method. The results show that children and parents co-construct segments of discourse with coherence relations across several conversational turns, by that way they build together a simplified framework for understanding the logic of human acts and their intention. For example, when the father and his children co-constructed coherent segments of discourse with the result relation between them, they completed the particular intention understanding at the same time. This research helps in enriching the logic structure of artificial intelligence applications such as visual question answering models and enhancing their reasoning abilities.

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