Meaning is highly activity-specific, in that the action that a particular sequence of words is taken to perform is severely underdetermined in the absence of an overarching activity, or a 'language-game'. In this paper, we combine a formal, incremental model of interactional dynamics and contextual update - Dynamic Syntax and Type Theory with Records (DS-TTR) - with Reinforcement Learning for word selection. We show, using an implemented system, that trial and error generation with a DS-TTR lexicon - a process we have dubbed babbling - leads to particular domain-specific dialogue acts to be learned and routinised over time; and thus that higher level dialogue structures - or how actions fit together to form a coherent whole - can be learned in this fashion. This method therefore allows incremental dialogue systems to be automatically bootstrapped from small amounts of unannotated dialogue transcripts, yet capturing a combinatorially large number of interactional variations. Even when the system is trained from only a single dialogue, we show that it supports over 8000 new dialogues in the same domain. This generalisation property results from the structural knowledge and constraints present within the grammar, and highlights limitations of recent state-of-the-art systems that are built using machine learning techniques only.
|Number of pages||5|
|Journal||CEUR Workshop Proceedings|
|Publication status||Published - 2017|
ASJC Scopus subject areas
- Computer Science(all)