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Flip through any book or listen to any song lyrics, and you will come acrosspronouns that, in certain cases, can hinder meaning comprehension, especiallyfor machines. As the role of having cognitive machines becomes pervasive in ourlives, numerous systems have been developed to resolve pronouns under variouschallenges. Commensurate with this, it is believed that having systems able todisambiguate pronouns in sentences will help towards the endowment of machineswith commonsense and reasoning abilities like those found in humans. However,one problem these systems face with modern English is the lack of genderpronouns, where people try to alternate by using masculine, feminine, or pluralto avoid the whole issue. Since humanity aims to the building of systems in thefull-bodied sense we usually reserve for people, what happens when pronouns inwritten text, like plural or epicene ones, refer to unspecified entities whosegender is not necessarily known? Wouldn't that put extra barriers to existingcoreference resolution systems? Towards answering those questions, through theimplementation of a neural-symbolic system that utilizes the best of bothworlds, we are employing PronounFlow, a system that reads any English sentencewith pronouns and entities, identifies which of them are not tied to eachother, and makes suggestions on which to use to avoid biases. Undertakenexperiments show that PronounFlow not only alternates pronouns in sentencesbased on the collective human knowledge around us but also considerably helpscoreference resolution systems with the pronoun disambiguation process.

PronounFlow: A Hybrid Approach for Calibrating Pronouns in Sentences