학술논문

A Call for Standardization and Validation of Text Style Transfer Evaluation
Document Type
Working Paper
Source
Subject
Computer Science - Machine Learning
Computer Science - Computation and Language
Language
Abstract
Text Style Transfer (TST) evaluation is, in practice, inconsistent. Therefore, we conduct a meta-analysis on human and automated TST evaluation and experimentation that thoroughly examines existing literature in the field. The meta-analysis reveals a substantial standardization gap in human and automated evaluation. In addition, we also find a validation gap: only few automated metrics have been validated using human experiments. To this end, we thoroughly scrutinize both the standardization and validation gap and reveal the resulting pitfalls. This work also paves the way to close the standardization and validation gap in TST evaluation by calling out requirements to be met by future research.
Comment: Accepted to Findings of ACL 2023