@inproceedings{zhang2022a,
author = {Zhang, Zhihan and Yu, Wenhao and Zhu, Chenguang and Jiang, Meng},
title = {A Unified Encoder-Decoder Framework with Entity Memory},
booktitle = {Empirical Methods in Natural Language Processing (EMNLP), Abu Dhabi, the United Arab Emirates, 2022},
year = {2022},
month = {December},
abstract = {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.},
url = {http://approjects.co.za/?big=en-us/research/publication/a-unified-encoder-decoder-framework-with-entity-memory/},
}