Evaluation Metrics for Text Data Augmentation in NLP
Marcellus Amadeus William Alberto Cruz Castañeda
Recent surveys on data augmentation for natural language processing have
reported different techniques and advancements in the field. Several
frameworks, tools, and repositories promote the implementation of text data
augmentation pipelines. However, a lack of evaluation criteria and standards
for method comparison due to different tasks, metrics, datasets, architectures,
and experimental settings makes comparisons meaningless. Also, a lack of
methods unification exists and text data augmentation research would benefit
from unified metrics to compare different augmentation methods. Thus, academics
and the industry endeavor relevant evaluation metrics for text data
augmentation techniques. The contribution of this work is to provide a taxonomy
of evaluation metrics for text augmentation methods and serve as a direction
for a unified benchmark. The proposed taxonomy organizes categories that
include tools for implementation and metrics calculation. Finally, with this
study, we intend to present opportunities to explore the unification and
standardization of text data augmentation metrics.