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