A Model of Anaphoric Ambiguities using Sheaf Theoretic Quantum-like Contextuality and BERT

Kin Ian Lo
(University College London, London, UK)
Mehrnoosh Sadrzadeh
(University College London, London, UK)
Shane Mansfield
(Quandela, Paris, France)

Ambiguities of natural language do not preclude us from using it and context helps in getting ideas across. They, nonetheless, pose a key challenge to the development of competent machines to understand natural language and use it as humans do. Contextuality is an unparalleled phenomenon in quantum mechanics, where different mathematical formalisms have been put forwards to understand and reason about it. In this paper, we construct a schema for anaphoric ambiguities that exhibits quantum-like contextuality. We use a recently developed criterion of sheaf-theoretic contextuality that is applicable to signalling models. We then take advantage of the neural word embedding engine BERT to instantiate the schema to natural language examples and extract probability distributions for the instances. As a result, plenty of sheaf-contextual examples were discovered in the natural language corpora BERT utilises. Our hope is that these examples will pave the way for future research and for finding ways to extend applications of quantum computing to natural language processing.

In Michael Moortgat and Gijs Wijnholds: Proceedings End-to-End Compositional Models of Vector-Based Semantics (E2ECOMPVEC), NUI Galway, 15-16 August 2022, Electronic Proceedings in Theoretical Computer Science 366, pp. 23–34.
Published: 10th August 2022.

ArXived at: https://dx.doi.org/10.4204/EPTCS.366.5 bibtex PDF
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