  We employ imitation learning to train a neural transition-based string
transducer for morphological tasks such as inflection generation and
lemmatization. Previous approaches to training this type of model either rely
on an external character aligner for the production of gold action sequences,
which results in a suboptimal model due to the unwarranted dependence on a
single gold action sequence despite spurious ambiguity, or require warm
starting with an MLE model. Our approach only requires a simple expert policy,
eliminating the need for a character aligner or warm start. It also addresses
familiar MLE training biases and leads to strong and state-of-the-art
performance on several benchmarks.
