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As part of the recent research effort on quantum natural language processing(QNLP), variational quantum sentence classifiers (VQSCs) have been implementedand supported in lambeq / DisCoPy, based on the DisCoCat model of sentencemeaning. We discuss in some detail VQSCs, including category theory, DisCoCatfor modeling sentence as string diagram, and DisCoPy for encoding stringdiagram as parameterized quantum circuit. Many NLP tasks, however, require thehandling of text consisting of multiple sentences, which is not supported inlambeq / DisCoPy. A good example is sentiment classification of customerfeedback or product review. We discuss three potential approaches tovariational quantum text classifiers (VQTCs), in line with VQSCs. The first isa weighted bag-of-sentences approach which treats text as a group ofindependent sentences with task-specific sentence weighting. The second is acoreference resolution approach which treats text as a consolidation of itsmember sentences with coreferences among them resolved. Both approaches arebased on the DisCoCat model and should be implementable in lambeq / DisCoCat.The third approach, on the other hand, is based on the DisCoCirc model whichconsiders both ordering of sentences and interaction of words in composing textmeaning from word and sentence meanings. DisCoCirc makes fundamentalmodification of DisCoCat since a sentence in DisCoCirc updates meanings ofwords, whereas all meanings are static in DisCoCat. It is not clear ifDisCoCirc can be implemented in lambeq / DisCoCat without breaking DisCoCat.

Variational Quantum Classifiers for Natural-Language Text