  Text normalization (TN) and inverse text normalization (ITN) are essential
preprocessing and postprocessing steps for text-to-speech synthesis and
automatic speech recognition, respectively. Many methods have been proposed for
either TN or ITN, ranging from weighted finite-state transducers to neural
networks. Despite their impressive performance, these methods aim to tackle
only one of the two tasks but not both. As a result, in a complete spoken
dialog system, two separate models for TN and ITN need to be built. This
heterogeneity increases the technical complexity of the system, which in turn
increases the cost of maintenance in a production setting. Motivated by this
observation, we propose a unified framework for building a single neural duplex
system that can simultaneously handle TN and ITN. Combined with a simple but
effective data augmentation method, our systems achieve state-of-the-art
results on the Google TN dataset for English and Russian. They can also reach
over 95% sentence-level accuracy on an internal English TN dataset without any
additional fine-tuning. In addition, we also create a cleaned dataset from the
Spoken Wikipedia Corpora for German and report the performance of our systems
on the dataset. Overall, experimental results demonstrate the proposed duplex
text normalization framework is highly effective and applicable to a range of
domains and languages
