
09:15 - 10:00 Invited Talk (Presenter: Aurelie Herbelot)
Title: How to refer like a god: aligning speaker-dependent meaning through formal distributional representations
Abstract: Reference -- the ability to talk about things -- is one of the most fundamental features of human language. Still, we are far from understanding how this ability comes about. In formal semantics, reference is theoretically well-defined but fails to provide a generic account of learnability. In data-driven approaches such as distributional semantics, the very notion of entity and event remains undefined, making those accounts unsuitable to even conceptualise reference. In this talk, I will hypothesise omniscient individuals with a fully shared lexicon and formal semantic model, showing that in such a 'godly' setup, reference can trivially be defined as perfect semantic alignment between interlocutors. Turning to a more realistic setting, I will then propose that distributional semantics provides the necessary tools to infer speaker-dependent models that are as close as possible to that idealisation. The suggested framework requires a tight integration of formal and distributional accounts at the representational level, whilst capitalising on the learning algorithms specific to distributional approaches.
Title: How to refer like a god: aligning speaker-dependent meaning through formal distributional representations
Abstract: Reference -- the ability to talk about things -- is one of the most fundamental features of human language. Still, we are far from understanding how this ability comes about. In formal semantics, reference is theoretically well-defined but fails to provide a generic account of learnability. In data-driven approaches such as distributional semantics, the very notion of entity and event remains undefined, making those accounts unsuitable to even conceptualise reference. In this talk, I will hypothesise omniscient individuals with a fully shared lexicon and formal semantic model, showing that in such a 'godly' setup, reference can trivially be defined as perfect semantic alignment between interlocutors. Turning to a more realistic setting, I will then propose that distributional semantics provides the necessary tools to infer speaker-dependent models that are as close as possible to that idealisation. The suggested framework requires a tight integration of formal and distributional accounts at the representational level, whilst capitalising on the learning algorithms specific to distributional approaches.