Work

Reinforcement learning of utterance semantics for speech-assisted cognitive user interfaces

Year: 2025

Type: article

Abstract: Talk at session "Language Meaning: between Symbolic and Statistical Modelling" of the Leibniz MMS Days 2025. Recognizing a basic difference between the semiotics of humans and machines presents a possibility to overcome the shortcomings of current artificial intelligence (AI) assistive devices. For the machine, the meaning of a (human) utterance is defined by its own scope of actions. Machines, thus, do not need to understand the conventional meaning of an utterance. Rather, they draw conversational implicatures in the sense of (neo-)Gricean pragmatics. For AI assistive devices, the learning of machine-specific meanings of human utterances, i.e. the fossilization of conversational implicatures into conventionalized ones by trial and error through lexicalization appears to be sufficient. Using the quite trivial example of a cognitive heating device, we show that—based on dynamic semantics—this process can be formalized as the reinforcement learning of utterance-meaning pairs (UMP).

Source: Zenodo (CERN European Organization for Nuclear Research)

DOI: 10.5281/zenodo.15131231

Authors: beim Graben, Peter, Huber-Liebl, Markus, Klimczak, Peter, Wirsching, Günther

Institutions: Bernstein Center for Computational Neuroscience Berlin, Brandenburg University of Technology Cottbus-Senftenberg, Catholic University of Eichstätt-Ingolstadt

Language: English

Topic: Speech and dialogue systems

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

OpenAlex ID: W6949636844

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