Browsing by Author "Eloff, Kevin"

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    Learning to speak and hear through multi-agent communication over a continuous acoustic channel
    (Stellenbosch : Stellenbosch University, 2023-03) Eloff, Kevin; Kamper, Herman; Engelbrecht, Herman; Stellenbosch University. Faculty of Engineering. Dept. of Electrical and Electronic Engineering.
    ENGLISH ABSTRACT: Human infants acquire language in large part through continuous signalling with their caregivers. By interacting and communicating with their caregivers, infants can observe the consequences of their communicative attempts (e.g. through parental response) that may guide the process of language acquisition. We find many similarities between human language acquisition and the intuition of intrinsic motivation which serves as a basis of reinforcement learning. In contrast, current trends in natural language processing disregard this, instead focusing on having larger models and more data to learn the statistical relationships between words with none of the original goals of language in mind. Multi-agent reinforcement learning has proven effective for investigating emergent communication between social agents. Most of these studies, however, focus on communication with discrete symbols. Humans learn language over a continuous channel and language has evolved through gestures and spoken communication, both of which are inherently continuous. This channel is also time-varying: interactions take place in unique settings with different channel acoustics and types of noise. These intricacies are lost when agents communicate directly with purely discrete symbols. We therefore ask: are we able to observe emergent language between agents with a continuous communication channel? And if so, how does learned continuous communication differ from discrete communication? Our objective is to provide a platform to study emergent continuous signalling in order to see how it relates to human language acquisition and evolution. We propose a messaging environment where a Speaker agent needs to convey a set of attributes to a Listener over a noisy acoustic channel. This thesis makes two core contributions. Firstly, in contrast to recent studies on language emergence, we train our agents with deep Q-learning rather than REINFORCE. When using DQN, we show significant performance gains and improved compositionality. Secondly, we provide a platform to study spoken emergent language between agents. To showcase this, we compare discrete and acoustic emergent languages. We show that, unlike the discrete case, the acoustic Speaker learns redundancy to improve Listener coherency when longer sequences are allowed. We also find that the acoustic Speaker develops more compositional communication protocols which implicitly compensates for transmission errors over the noisy channel. In addition, we show early experiments with promising results in language grounding (to English) and effective generalisation to real-world communication channels.