  Referring Expression Generation (REG) is the task of generating contextually
appropriate references to entities. A limitation of existing REG systems is
that they rely on entity-specific supervised training, which means that they
cannot handle entities not seen during training. In this study, we address this
in two ways. First, we propose task setups in which we specifically test a REG
system's ability to generalize to entities not seen during training. Second, we
propose a profile-based deep neural network model, ProfileREG, which encodes
both the local context and an external profile of the entity to generate
reference realizations. Our model generates tokens by learning to choose
between generating pronouns, generating from a fixed vocabulary, or copying a
word from the profile. We evaluate our model on three different splits of the
WebNLG dataset, and show that it outperforms competitive baselines in all
settings according to automatic and human evaluations.
