  We describe our participation in the Word Segmentation and Morphological
Parsing (WSMP) for Sanskrit hackathon. We approach the word segmentation task
as a sequence labelling task by predicting edit operations from which
segmentations are derived. We approach the morphological analysis task by
predicting morphological tags and rules that transform inflected words into
their corresponding stems. Also, we propose an end-to-end trainable pipeline
model for joint segmentation and morphological analysis. Our model performed
best in the joint segmentation and analysis subtask (80.018 F1 score) and
performed second best in the individual subtasks (segmentation: 96.189 F1 score
/ analysis: 69.180 F1 score).
  Finally, we analyse errors made by our models and suggest future work and
possible improvements regarding data and evaluation.
