29 research outputs found
Computational Models of Tutor Feedback in Language Acquisition
This paper investigates the role of tutor feedback in language learning using
computational models. We compare two dominant paradigms in language learning:
interactive learning and cross-situational learning - which differ primarily in
the role of social feedback such as gaze or pointing. We analyze the
relationship between these two paradigms and propose a new mixed paradigm that
combines the two paradigms and allows to test algorithms in experiments that
combine no feedback and social feedback. To deal with mixed feedback
experiments, we develop new algorithms and show how they perform with respect
to traditional knn and prototype approaches.Comment: 6 pages, 8 figures, Seventh Joint IEEE International Conference on
Development and Learning and on Epigenetic Robotic
A Practical Guide to Studying Emergent Communication through Grounded Language Games
The question of how an effective and efficient communication system can
emerge in a population of agents that need to solve a particular task attracts
more and more attention from researchers in many fields, including artificial
intelligence, linguistics and statistical physics. A common methodology for
studying this question consists of carrying out multi-agent experiments in
which a population of agents takes part in a series of scripted and
task-oriented communicative interactions, called 'language games'. While each
individual language game is typically played by two agents in the population, a
large series of games allows the population to converge on a shared
communication system. Setting up an experiment in which a rich system for
communicating about the real world emerges is a major enterprise, as it
requires a variety of software components for running multi-agent experiments,
for interacting with sensors and actuators, for conceptualising and
interpreting semantic structures, and for mapping between these semantic
structures and linguistic utterances. The aim of this paper is twofold. On the
one hand, it introduces a high-level robot interface that extends the Babel
software system, presenting for the first time a toolkit that provides flexible
modules for dealing with each subtask involved in running advanced grounded
language game experiments. On the other hand, it provides a practical guide to
using the toolkit for implementing such experiments, taking a grounded colour
naming game experiment as a didactic example.Comment: This paper was officially published at the 'Language Learning for
Artificial Agents (L2A2) Symposium' of the 2019 Artificial Intelligence and
Simulation of Behaviour (AISB) Conventio
Neural heuristics for scaling constructional language processing
Constructionist approaches to language make use of form-meaning pairings, called constructions, to capture all linguistic knowledge that is necessary for comprehending and producing natural language expressions. Language processing consists then in combining the constructions of a grammar in such a way that they solve a given language comprehension or production problem. Finding such an adequate sequence of constructions constitutes a search problem that is combinatorial in nature and becomes intractable as grammars increase in size. In this paper, we introduce a neural methodology for learning heuristics that substantially optimise the search processes involved in constructional language processing. We validate the methodology in a case study for the CLEVR benchmark dataset. We show that our novel methodology outperforms state-of-the-art techniques in terms of size of the search space and time of computation, most markedly in the production direction. The results reported on in this paper have the potential to overcome the major efficiency obstacle that hinders current efforts in learning large-scale construction grammars, thereby contributing to the development of scalable constructional language processing systems