Applying Distributional Compositional Categorical Models of Meaning to Language Translation
This work addresses translation challenges for Irish, a Gaelic language, but appears incremental as it applies existing models to a new language without major methodological breakthroughs.
The paper tackles the problem of language translation by using distributional compositional categorical models to compare sentence meanings between Irish and English via cosine similarity, and proposes a procedure for translating nouns based on conceptual space models and metrics on ConvexRel to measure concept distances.
The aim of this paper is twofold: first we will use vector space distributional compositional categorical models of meaning to compare the meaning of sentences in Irish and in English (and thus ascertain when a sentence is the translation of another sentence) using the cosine similarity score. Then we shall outline a procedure which translates nouns by understanding their context, using a conceptual space model of cognition. We shall use metrics on the category ConvexRel to determine the distance between concepts (and determine when a noun is the translation of another noun). This paper will focus on applications to Irish, a member of the Gaelic family of languages.