  Popular solutions to Named Entity Recognition (NER) include conditional
random fields, sequence-to-sequence models, or utilizing the question-answering
framework. However, they are not suitable for nested and overlapping spans with
large ontologies and for predicting the position of the entities. To fill this
gap, we introduce a new model for NER task -- an RNN transducer (RNN-T). These
models are trained using paired input and output sequences without explicitly
specifying the alignment between them, similar to other seq-to-seq models.
RNN-T models learn the alignment using a loss function that sums over all
alignments. In NER tasks, however, the alignment between words and target
labels are available from the human annotations. We propose a fixed alignment
RNN-T model that utilizes the given alignment, while preserving the benefits of
RNN-Ts such as modeling output dependencies. As a more general case, we also
propose a constrained alignment model where users can specify a relaxation of
the given input alignment and the model will learn an alignment within the
given constraints. In other words, we propose a family of seq-to-seq models
which can leverage alignments between input and target sequences when
available. Through empirical experiments on a challenging real-world medical
NER task with multiple nested ontologies, we demonstrate that our fixed
alignment model outperforms the standard RNN-T model, improving F1-score from
0.70 to 0.74.
