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.

该研究提出了一种面向上下文的实体参照生成模型，并解决了现有模型依赖于特定实体训练数据的局限性，同时在 WebNLG 数据集上进行的实验表明，该模型在多个方面有着优越的表现。