Sketching a Linguistically-Driven Reasoning Dialog Model for Social Talk
Alex Lưu · 2022
The capability of holding social talk (or casual conversation) and making sense of conversational content requires context-sensitive natural language understanding and reasoning, which cannot be handled efficiently by the current popular open-domain dialog systems and chatbots.Heavily relying on corpus-based machine learning techniques to encode and decode context-sensitive meanings, these systems focus on fitting a particular training dataset, but not tracking what is actually happening in a conversation, and therefore easily derail in a new context.This work sketches out a more linguistically-informed architecture to handle social talk in English, in which corpus-based methods form the backbone of the relatively context-insensitive components (e.g.part-ofspeech tagging, approximation of lexical meaning and constituent chunking), while symbolic modeling is used for reasoning out the contextsensitive components, which do not have any consistent mapping to linguistic forms.All components are fitted into a Bayesian gametheoretic model to address the interactive and rational aspects of conversation. 1