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.- Anthology ID:
- 2022.emnlp-main.43
- Volume:
- Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
- Month:
- December
- Year:
- 2022
- Address:
- Abu Dhabi, United Arab Emirates
- Venue:
- EMNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 689–705
- Language:
- URL:
- https://aclanthology.org/2022.emnlp-main.43
- DOI:
- Cite (ACL):
- Zhihan Zhang, Wenhao Yu, Chenguang Zhu, and Meng Jiang. 2022. A Unified Encoder-Decoder Framework with Entity Memory. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 689–705, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.
- Cite (Informal):
- A Unified Encoder-Decoder Framework with Entity Memory (Zhang et al., EMNLP 2022)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/2022.emnlp-main.43.pdf