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Kin Ian Lo,Mehrnoosh Sadrzadeh,Shane Mansfield

from arxiv, In Proceedings E2ECOMPVEC, arXiv:2208.05313

Ambiguities of natural language do not preclude us from using it and context helps in getting ideas across. They, nonetheless, pose a key challenge to the development of competent machines to understand natural language and use it as humans do. Contextuality is an unparalleled phenomenon in quantum mechanics, where different mathematical formalisms have been put forwards to understand and reason about it. In this paper, we construct a schema for anaphoric ambiguities that exhibits quantum-like contextuality. We use a recently developed criterion of sheaf-theoretic contextuality that is applicable to signalling models. We then take advantage of the neural word embedding engine BERT to instantiate the schema to natural language examples and extract probability distributions for the instances. As a result, plenty of sheaf-contextual examples were discovered in the natural language corpora BERT utilises. Our hope is that these examples will pave the way for future research and for finding ways to extend applications of quantum computing to natural language processing.

翻译：自然语言的模糊性并不排除我们使用自然语言, 环境的模糊性可以帮助我们获得各种想法。但是, 自然语言对于开发能理解自然语言并将自然语言作为人使用的能力机器来说是一个关键的挑战。环境是量子力学中一个无与伦比的现象, 在量子力学中, 提出了不同的数学形式主义来理解和理解自然语言。在本文中, 我们构筑了一种能显示量子性背景特性的古代模糊性模型。我们使用最近开发的用于信号模型的隐性理论背景质量标准。然后, 我们利用嵌入神经字的引擎 BERT 来将自然语言的模型瞬间化, 并提取这些实例的概率分布。结果, 在自然语言Coricora BERT 中发现了大量的外文学实例。我们希望这些实例将为未来的研究铺平道路, 并找到将量子计算的应用扩大到自然语言处理的方法。