Phil Ostheimer

(AG Machine Learning)
hosted by PhD Program in CS @ TU KL

"Style Classification for Text Based on Syntax"

Text style transfer (TST) aims to change a text's style from source to target style while retaining the content. Existing TST methods are usually evaluated regarding content preservation, fluency, and style transfer accuracy. In this paper, we focus on the latter aspect. We show that existing measures of style transfer accuracy partly also measure content difference. We present a new measure based on a sentence's syntactic structure. It uses part-of-speech (POS) tags instead of the text itself. This ensures that the new measure focuses on the syntactic structure (as an identifier of a given style) instead of leveraging both style and content. Our experiments show that our new method serves as a competitive style classifier when the styles are sufficiently different in their syntactic structures.

Time: Monday, 10.01.2022, 16:00

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