  Entities, as important carriers of real-world knowledge, play a key role in
many NLP tasks. We focus on incorporating entity knowledge into an
encoder-decoder framework for informative text generation. Existing approaches
tried to index, retrieve, and read external documents as evidence, but they
suffered from a large computational overhead. In this work, we propose an
encoder-decoder framework with an entity memory, namely EDMem. The entity
knowledge is stored in the memory as latent representations, and the memory is
pre-trained on Wikipedia along with encoder-decoder parameters. To precisely
generate entity names, we design three decoding methods to constrain entity
generation by linking entities in the memory. EDMem is a unified framework that
can be used on various entity-intensive question answering and generation
tasks. Extensive experimental results show that EDMem outperforms both
memory-based auto-encoder models and non-memory encoder-decoder models.
