Dual Encoder Classifier Models as Constraints in Neural Text Normalization
Abstract
Neural text normalization systems achieve
high accuracy, but the errors they do make can
include not only “acceptable” errors (such as
reading $3 as three dollar) but also unacceptable
errors (reading $3 as three euros). We explore
ways of training dual encoder classifiers
with both positive and negative data to then
use as soft constraints in neural text normalization
in order to decrease the number of unacceptable
errors. Already-low error rates and
high variability in performance on the evaluation
set make it difficult to determine when improvement
is significant, but qualitative analysis
suggests that certain types of dual encoder
constraints yield systems that make fewer unacceptable
errors.
high accuracy, but the errors they do make can
include not only “acceptable” errors (such as
reading $3 as three dollar) but also unacceptable
errors (reading $3 as three euros). We explore
ways of training dual encoder classifiers
with both positive and negative data to then
use as soft constraints in neural text normalization
in order to decrease the number of unacceptable
errors. Already-low error rates and
high variability in performance on the evaluation
set make it difficult to determine when improvement
is significant, but qualitative analysis
suggests that certain types of dual encoder
constraints yield systems that make fewer unacceptable
errors.
Research Areas
