  Most problems in natural language processing can be approximated as inverse
problems such as analysis and generation at variety of levels from
morphological (e.g., cat+Plural <-> cats) to semantic (e.g., (call + 1 2) <->
"Calculate one plus two."). Although the tasks in both directions are closely
related, general approach in the field has been to design separate models
specific for each task. However, having one shared model for both tasks, would
help the researchers exploit the common knowledge among these problems with
reduced time and memory requirements. We investigate a specific class of neural
networks, called Invertible Neural Networks (INNs) (Ardizzone et al. 2019) that
enable simultaneous optimization in both directions, hence allow addressing of
inverse problems via a single model. In this study, we investigate INNs on
morphological problems casted as inverse problems. We apply INNs to various
morphological tasks with varying ambiguity and show that they provide
competitive performance in both directions. We show that they are able to
recover the morphological input parameters, i.e., predicting the lemma (e.g.,
cat) or the morphological tags (e.g., Plural) when run in the reverse
direction, without any significant performance drop in the forward direction,
i.e., predicting the surface form (e.g., cats).
