Year: 2023
Type: conference-paper
Abstract: Guided by grammatical structure, words compose to form sentences, and guided by discourse structure, sentences compose to form dialogues and documents.The compositional aspect of sentence and discourse units is often overlooked by machine learning algorithms.A recent initiative called Quantum Natural Language Processing (QNLP) learns word meanings as points in a Hilbert space and acts on them via a translation of grammatical structure into Parametrised Quantum Circuits (PQCs).Previous work extended the QNLP translation to discourse structure using points in a closure of Hilbert spaces.In this paper, we evaluate this translation on a Winograd-style pronoun resolution task.We train a Variational Quantum Classifier (VQC) for binary classification and implement an end-to-end pronoun resolution system.The simulations executed on IBMQ software converged with an F1 score of 87.20%.The model outperformed two out of three classical coreference resolution systems and neared state-of-the-art SpanBERT.A mixed quantumclassical model yet improved these results with an F1 score increase of around 6%.
Authors: Hadi Wazni, Mehrnoosh Sadrzadeh
Institution: University College London
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
Funder: Royal Academy of Engineering
Award: G7407443353
OpenAlex ID: W4389519432