Towards Transparency in Coreference Resolution: A Quantum-Inspired
  Approach
Hadi Wazni Mehrnoosh Sadrzadeh
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 quantum-classical model yet improved these
results with an F1 score increase of around 6%.