@inproceedings{zhang-etal-2022-unified,
title = "A Unified Encoder-Decoder Framework with Entity Memory",
author = "Zhang, Zhihan and
Yu, Wenhao and
Zhu, Chenguang and
Jiang, Meng",
editor = "Goldberg, Yoav and
Kozareva, Zornitsa and
Zhang, Yue",
booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing",
month = dec,
year = "2022",
address = "Abu Dhabi, United Arab Emirates",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.emnlp-main.43",
doi = "10.18653/v1/2022.emnlp-main.43",
pages = "689--705",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T A Unified Encoder-Decoder Framework with Entity Memory
%A Zhang, Zhihan
%A Yu, Wenhao
%A Zhu, Chenguang
%A Jiang, Meng
%Y Goldberg, Yoav
%Y Kozareva, Zornitsa
%Y Zhang, Yue
%S Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
%D 2022
%8 December
%I Association for Computational Linguistics
%C Abu Dhabi, United Arab Emirates
%F zhang-etal-2022-unified
%X 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.
%R 10.18653/v1/2022.emnlp-main.43
%U https://aclanthology.org/2022.emnlp-main.43
%U https://doi.org/10.18653/v1/2022.emnlp-main.43
%P 689-705
Markdown (Informal)
[A Unified Encoder-Decoder Framework with Entity Memory](https://aclanthology.org/2022.emnlp-main.43) (Zhang et al., EMNLP 2022)
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.