Flip through any book or listen to any song lyrics, and you will come across pronouns that, in certain cases, can hinder meaning comprehension, especially for machines. As the role of having cognitive machines becomes pervasive in our lives, numerous systems have been developed to resolve pronouns under various challenges. Commensurate with this, it is believed that having systems able to disambiguate pronouns in sentences will help towards the endowment of machines with commonsense and reasoning abilities like those found in humans. However, one problem these systems face with modern English is the lack of gender pronouns, where people try to alternate by using masculine, feminine, or plural to avoid the whole issue. Since humanity aims to the building of systems in the full-bodied sense we usually reserve for people, what happens when pronouns in written text, like plural or epicene ones, refer to unspecified entities whose gender is not necessarily known? Wouldn't that put extra barriers to existing coreference resolution systems? Towards answering those questions, through the implementation of a neural-symbolic system that utilizes the best of both worlds, we are employing PronounFlow, a system that reads any English sentence with pronouns and entities, identifies which of them are not tied to each other, and makes suggestions on which to use to avoid biases. Undertaken experiments show that PronounFlow not only alternates pronouns in sentences based on the collective human knowledge around us but also considerably helps coreference resolution systems with the pronoun disambiguation process.

翻译：翻阅任何一本书或聆听任何一首歌词，你都会遇到在某些情况下可能阻碍意义理解的代词，尤其是对机器而言。随着认知机器在我们的生活中扮演越来越普遍的角色，众多系统已被开发出来，用于在各种挑战下解析代词。与此相应的是，人们认为拥有能消除句子中代词歧义的系统，有助于赋予机器像人类那样的常识和推理能力。然而，这些系统在现代英语中面临的一个问题是性别代词的缺失，人们试图通过交替使用阳性、阴性或复数代词来回避整个问题。既然人类的目标是构建通常专属于人类的全方位意义上的系统，那么当书面文本中的代词（如复数代词或通性代词）指向性别不一定已知的未指定实体时，会发生什么？这难道不会给现有的共指消解系统增加额外障碍吗？为了回答这些问题，通过实现一个结合两者优势的神经符号系统，我们采用了PronounFlow——一个能阅读任何包含代词和实体的英语句子，识别哪些代词与实体之间没有关联，并建议使用哪些代词以避免偏见的系统。进行的实验表明，PronounFlow不仅基于我们周围的人类集体知识交替使用句子中的代词，还显著帮助共指消解系统进行代词消歧过程。