Autonomization of Monoidal Categories
This work addresses a foundational problem in computational linguistics by simplifying the theoretical framework for modeling natural language, potentially enabling more flexible and diverse semantic models.
The paper challenges the DisCoCat community's belief by demonstrating that a monoidal category alone suffices to define categorical compositional models of natural language, broadening the range of distributional semantic models to include non-linear maps and cartesian products.
We show that contrary to common belief in the DisCoCat community, a monoidal category is all that is needed to define a categorical compositional model of natural language. This relies on a construction which freely adds adjoints to a monoidal category. In the case of distributional semantics, this broadens the range of available models, to include non-linear maps and cartesian products for instance. We illustrate the applications of this principle to various distributional models of meaning.