Computational and Robotic Models of Early Language Development: A Review

We review computational and robotics models of early language learning and development. We first explain why and how these models are used to understand better how children learn language. We argue that they provide concrete theories of language learning as a complex dynamic system, complementing traditional methods in psychology and linguistics. We review different modeling formalisms, grounded in techniques from machine learning and artificial intelligence such as Bayesian and neural network approaches. We then discuss their role in understanding several key mechanisms of language development: cross-situational statistical learning, embodiment, situated social interaction, intrinsically motivated learning, and cultural evolution. We conclude by discussing future challenges for research, including modeling of large-scale empirical data about language acquisition in real-world environments. Keywords: Early language learning, Computational and robotic models, machine learning, development, embodiment, social interaction, intrinsic motivation, self-organization, dynamical systems, complexity.Comment: to appear in International Handbook on Language Development, ed. J. Horst and J. von Koss Torkildsen, Routledg

Complexity Reduction in the Negotiation of New Lexical Conventions

In the process of collectively inventing new words for new concepts in a population, conflicts can quickly become numerous, in the form of synonymy and homonymy. Remembering all of them could cost too much memory, and remembering too few may slow down the overall process. Is there an efficient behavior that could help balance the two? The Naming Game is a multi-agent computational model for the emergence of language, focusing on the negotiation of new lexical conventions, where a common lexicon self-organizes but going through a phase of high complexity. Previous work has been done on the control of complexity growth in this particular model, by allowing agents to actively choose what they talk about. However, those strategies were relying on ad hoc heuristics highly dependent on fine-tuning of parameters. We define here a new principled measure and a new strategy, based on the beliefs of each agent on the global state of the population. The measure does not rely on heavy computation, and is cognitively plausible. The new strategy yields an efficient control of complexity growth, along with a faster agreement process. Also, we show that short-term memory is enough to build relevant beliefs about the global lexicon.Comment: Published at CogSci 2018 conferenc

Active Control of Complexity Growth in Naming Games: Hearer's Choice

International audienceHow do linguistic conventions emerge among a population of individuals? A shared lexicon can self-organize at this level through local interactions between individuals, as this has been modelled in the Naming Games computational framework. However, the dynamics of the convergence process towards this shared convention can differ a lot, depending on the interaction scenario. Infants, who acquire social conventions really fast, control actively the complexity of what they learn, often following a developmental pathway. Adults also adapt the complexity of their linguistic input when speaking to language beginners. We show here that such active learning mechanism can improve considerably the speed of language formation in Naming Game models. We compare two scenarios for the interactions: the speaker exherts an active control, or the hearer does. The second scenario shows faster dynamics, with more robustness

Active Learning Strategies and Active Control of Complexity Growth in Naming Games

International audienceNaming Games are models of the dynamic formation of lexical conventions in populations of agents. In this work we introduce new Naming Game strategies, using developmental and active learning mechanisms to control the growth of complexity. An information theoretical measure to compare those strategies is introduced, and used to study their impact on the dynamics of the Naming Game

Scaling the impact of active topic choice in the Naming Game

International audienceHow does language emerge, evolve and gets transmitted between individuals? What mechanisms underly the formation and evolution of linguistic conventions, and what are their dynamics? Computational linguistic studies have shown that local interactions within groups of individuals (e.g. humans or robots) can lead to self-organization of lexica associating semantic categories to words [13]. However, it still doesn't scale well to complex meaning spaces and a large number of possible word-meaning associations (or lexical conventions), suggesting high competition among those conventions. In statistical machine learning and in developmental sciences, it has been argued that an active control of the complexity of learning situations can have a significant impact on the global dynamics of the learning process [2, 4, 3]. This approach has been mostly studied for single robotic agents learning sensorimotor affordances [6, 5]. However active learning might represent an evolutionary advantage for language formation at the population level as well [8, 12]. Naming Games are a computational framework, elaborated to simulate the self-organization of lexical conventions in the form of a multi-agent model [11]. Through repeated local interactions between random couples of agents (designated speaker and hearer), shared conventions emerge. Interactions consist of uttering a word – or an abstract signal – referring to a topic, and evaluating the success or failure of communication. However, in existing works processes involved in these interactions are typically random choices, especially the choice of a communication topic. The introduction of active learning algorithms in these models produces significant improvement of the convergence process towards a shared vocabulary, with the speaker [7, 9, 1] or the hearer [10] actively controlling vocabulary growth. We study here how the convergence time and the maximum level of complexity scale with population size, for three different strategies (one with random topic choice and two with active topic choice) detailed in table 1. Both active strategies use a parameter (α and n), which is each time chosen optimal in our simulations. As for the version of the Naming Game used in our work, the scenario of the interaction is described in [10]. Vocabulary is updated as described in the Minimal Naming Game, detailed in [14]. In our simulations, we choose to set N = M = W , where N is the population size, M the number of meanings, and W the number of possible words. The computed theoretical success ratio of communication is used to represent the degree of convergence toward a shared lexicon for the whole population. A value of 1 means that the population reached full convergence. Complexity level of an individual lexicon is measured as the total number of distinct associations between meanings and words in the lexicon, or in other words: memory usage. We show here (see figures 2,3) that convergence time and maximum complexity are reduced with active topic choice, a behavior that is amplified as larger populations are considered. The minimal counts strategy yields a strictly minimum complexity (equal to the complexity of a completed lexicon), while converging as fast as the success threshold strategy. Further work will deal with other variants of the Naming Game (with different vocabulary update, population replacement, and different ratio for N , M and W). For the moment only the hearer's choice scenario is studied, because of its high robustness to changes in parameter values for the different strategies [10]

Complexity Reduction in the Negotiation of New Lexical Conventions

International audienceIn the process of collectively inventing new words for new concepts in a population, conflicts can quickly become numerous, in the form of synonymy and homonymy. Remembering all of them could cost too much memory, and remembering too few may slow down the overall process. Is there an efficient behavior that could help balance the two? The Naming Game is a multi-agent computational model for the emergence of language , focusing on the negotiation of new lexical conventions, where a common lexicon self-organizes but going through a phase of high complexity. Previous work has been done on the control of complexity growth in this particular model, by allowing agents to actively choose what they talk about. However , those strategies were relying on ad hoc heuristics highly dependent on fine-tuning of parameters. We define here a new principled measure and a new strategy, based on the beliefs of each agent on the global state of the population. The measure does not rely on heavy computation, and is cognitively plausible. The new strategy yields an efficient control of complexity growth, along with a faster agreement process. Also, we show that short-term memory is enough to build relevant beliefs about the global lexicon

Computational and Robotic Models of Early Language Development: A Review

International audienceWe review computational and robotics models of early language learning and development. We first explain why and how these models are used to understand better how children learn language. We argue that they provide concrete theories of language learning as a complex dynamic system, complementing traditional methods in psychology and linguistics. We review different modeling formalisms, grounded in techniques from machine learning and artificial intelligence such as Bayesian and neural network approaches. We then discuss their role in understanding several key mechanisms of language development: cross-situational statistical learning, embodiment, situated social interaction, intrinsically motivated learning, and cultural evolution. We conclude by discussing future challenges for research, including modeling of large-scale empirical data about language acquisition in real-world environments

Comparing Notes: Recording and Criticism

This chapter charts the ways in which recording has changed the nature of music criticism. It both provides an overview of the history of recording and music criticism, from the advent of Edison’s Phonograph to the present day, and examines the issues arising from this new technology and the consequent transformation of critical thought and practice