Russian requires speakers of the language to conceptualize events using temporal language devices such as Aktionsarten and aspect, which relate to particular profiles and characteristics of events such as whether the event just started, whether it is ongoing or it is a repeated event. This chapter explores how such temporal features of events can be processed and learned by robots through grounded situated interactions. We use a whole systems approach, tightly integrating perception, conceptualization grammatical processing and learning and demonstrate how a system of Aktionsarten can be acquired.
This chapter explores a possible language strategy for verbalizing aspect: the encoding of Aktionsarten by means of morphological markers. Russian tense-aspect system is used as a model. We first operationalize this system and reconstruct the learning operators needed for acquiring it. Then we perform a first language formation experiment in which a novel system of Aktionsarten emerges and gets coordinated between the agents, driven by a need for higher expressivity.
Over the past several decades, psycholinguists have gained countless insights into the process of child language acquisition. Can these findings be used for the development of language competence in autonomous artificial systems? This paper reports on our attempt to apply insights from developmental psychology in order to enable artificial systems to acquire language. We consider a comprehensive chain of computational processes, starting from conceptualization and extending through language generation and interpretation, and show how they can be intertwined to allow for acquisition of complex aspects of grammar.
Grammatical agreement is one of the most puzzling aspects found in natural language. Its acquisition requires intensive linguistic exposure and capacities to deal with outliers that break regular patterns. Other than relying on statistical methods to deal with agreement in a computational application, this paper demonstrates how agreement can be learned by artificial agents in a simulated environment in such a way that the openendedness of natural language can be captured by their language processing mechanisms.
Aspect is undoubtedly the most capricious grammatical category of the Russian language. It has often been asserted as a mystery accessible only to native speakers, leaving all the others lost in its apparently infinite clutter. Recent work in cognitive linguistics has tried to bring order to the seeming chaos of the Russian aspectual system. But these approaches have not been operationalized so far. This paper demonstrates how the aspectual derivation of Russian verbs can be handled successfully with Fluid Constructional Grammar, a computational formalism recently developed for the representation and processing of constructions.