  This paper presents the submission by the CMU-01 team to the SIGMORPHON 2019
task 2 of Morphological Analysis and Lemmatization in Context. This task
requires us to produce the lemma and morpho-syntactic description of each token
in a sequence, for 107 treebanks. We approach this task with a hierarchical
neural conditional random field (CRF) model which predicts each coarse-grained
feature (eg. POS, Case, etc.) independently. However, most treebanks are
under-resourced, thus making it challenging to train deep neural models for
them. Hence, we propose a multi-lingual transfer training regime where we
transfer from multiple related languages that share similar typology.
