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    <abstract>Access to external knowledge is essential for many natural language processing tasks, such as question answering and dialogue. Existing methods often rely on a parametric model that stores knowledge in its parameters, or use a retrieval-augmented model that has access to an external knowledge source. Parametric and retrieval-augmented models have complementary strengths in terms of computational efficiency and predictive accuracy. To combine the strength of both approaches, we propose the Efficient Memory-Augmented Transformer (EMAT) – it encodes external knowledge into a key-value memory and exploits the fast maximum inner product search for memory querying. We also introduce pre-training tasks that allow EMAT to encode informative key-value representations, and to learn an implicit strategy to integrate multiple memory slots into the transformer. Experiments on various knowledge-intensive tasks such as question answering and dialogue datasets show that, simply augmenting parametric models (T5-base) using our method produces more accurate results (e.g., 25.8 → 44.3 EM on NQ) while retaining a high throughput (e.g., 1000 queries/s on NQ). Compared to retrieval-augmented models, EMAT runs substantially faster across the board and produces more accurate results on WoW and ELI5.</abstract>
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%0 Conference Proceedings
%T An Efficient Memory-Augmented Transformer for Knowledge-Intensive NLP Tasks
%A Wu, Yuxiang
%A Zhao, Yu
%A Hu, Baotian
%A Minervini, Pasquale
%A Stenetorp, Pontus
%A Riedel, Sebastian
%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 wu-etal-2022-efficient
%X Access to external knowledge is essential for many natural language processing tasks, such as question answering and dialogue. Existing methods often rely on a parametric model that stores knowledge in its parameters, or use a retrieval-augmented model that has access to an external knowledge source. Parametric and retrieval-augmented models have complementary strengths in terms of computational efficiency and predictive accuracy. To combine the strength of both approaches, we propose the Efficient Memory-Augmented Transformer (EMAT) – it encodes external knowledge into a key-value memory and exploits the fast maximum inner product search for memory querying. We also introduce pre-training tasks that allow EMAT to encode informative key-value representations, and to learn an implicit strategy to integrate multiple memory slots into the transformer. Experiments on various knowledge-intensive tasks such as question answering and dialogue datasets show that, simply augmenting parametric models (T5-base) using our method produces more accurate results (e.g., 25.8 → 44.3 EM on NQ) while retaining a high throughput (e.g., 1000 queries/s on NQ). Compared to retrieval-augmented models, EMAT runs substantially faster across the board and produces more accurate results on WoW and ELI5.
%R 10.18653/v1/2022.emnlp-main.346
%U https://aclanthology.org/2022.emnlp-main.346
%U https://doi.org/10.18653/v1/2022.emnlp-main.346
%P 5184-5196

Markdown (Informal)

[An Efficient Memory-Augmented Transformer for Knowledge-Intensive NLP Tasks](https://aclanthology.org/2022.emnlp-main.346) (Wu et al., EMNLP 2022)

• An Efficient Memory-Augmented Transformer for Knowledge-Intensive NLP Tasks (Wu et al., EMNLP 2022)

ACL

• Yuxiang Wu, Yu Zhao, Baotian Hu, Pasquale Minervini, Pontus Stenetorp, and Sebastian Riedel. 2022. An Efficient Memory-Augmented Transformer for Knowledge-Intensive NLP Tasks. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 5184–5196, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.