@inproceedings{zhang-etal-2022-mixture,
title = "Mixture of Attention Heads: Selecting Attention Heads Per Token",
author = "Zhang, Xiaofeng and
Shen, Yikang and
Huang, Zeyu and
Zhou, Jie and
Rong, Wenge and
Xiong, Zhang",
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.278",
doi = "10.18653/v1/2022.emnlp-main.278",
pages = "4150--4162",
abstract = "Mixture-of-Experts (MoE) networks have been proposed as an efficient way to scale up model capacity and implement conditional computing. However, the study of MoE components mostly focused on the feedforward layer in Transformer architecture. This paper proposes the Mixture of Attention Heads (MoA), a new architecture that combines multi-head attention with the MoE mechanism. MoA includes a set of attention heads that each has its own set of parameters. Given an input, a router dynamically selects a subset of k attention heads per token. This conditional computation schema allows MoA to achieve stronger performance than the standard multi-head attention layer. Furthermore, the sparsely gated MoA can easily scale up the number of attention heads and the number of parameters while preserving computational efficiency. Despite performance improvements, MoA also automatically differentiates heads{'} utilities, providing a new perspective to discuss the model{'}s interpretability. We conducted experiments on several important tasks, including Machine Translation and Masked Language Modeling. Experiments have shown promising results on several tasks against strong baselines that involve large and very deep models.",
}
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<abstract>Mixture-of-Experts (MoE) networks have been proposed as an efficient way to scale up model capacity and implement conditional computing. However, the study of MoE components mostly focused on the feedforward layer in Transformer architecture. This paper proposes the Mixture of Attention Heads (MoA), a new architecture that combines multi-head attention with the MoE mechanism. MoA includes a set of attention heads that each has its own set of parameters. Given an input, a router dynamically selects a subset of k attention heads per token. This conditional computation schema allows MoA to achieve stronger performance than the standard multi-head attention layer. Furthermore, the sparsely gated MoA can easily scale up the number of attention heads and the number of parameters while preserving computational efficiency. Despite performance improvements, MoA also automatically differentiates heads’ utilities, providing a new perspective to discuss the model’s interpretability. We conducted experiments on several important tasks, including Machine Translation and Masked Language Modeling. Experiments have shown promising results on several tasks against strong baselines that involve large and very deep models.</abstract>
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%0 Conference Proceedings
%T Mixture of Attention Heads: Selecting Attention Heads Per Token
%A Zhang, Xiaofeng
%A Shen, Yikang
%A Huang, Zeyu
%A Zhou, Jie
%A Rong, Wenge
%A Xiong, Zhang
%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-mixture
%X Mixture-of-Experts (MoE) networks have been proposed as an efficient way to scale up model capacity and implement conditional computing. However, the study of MoE components mostly focused on the feedforward layer in Transformer architecture. This paper proposes the Mixture of Attention Heads (MoA), a new architecture that combines multi-head attention with the MoE mechanism. MoA includes a set of attention heads that each has its own set of parameters. Given an input, a router dynamically selects a subset of k attention heads per token. This conditional computation schema allows MoA to achieve stronger performance than the standard multi-head attention layer. Furthermore, the sparsely gated MoA can easily scale up the number of attention heads and the number of parameters while preserving computational efficiency. Despite performance improvements, MoA also automatically differentiates heads’ utilities, providing a new perspective to discuss the model’s interpretability. We conducted experiments on several important tasks, including Machine Translation and Masked Language Modeling. Experiments have shown promising results on several tasks against strong baselines that involve large and very deep models.
%R 10.18653/v1/2022.emnlp-main.278
%U https://aclanthology.org/2022.emnlp-main.278
%U https://doi.org/10.18653/v1/2022.emnlp-main.278
%P 4150-4162
Markdown (Informal)
[Mixture of Attention Heads: Selecting Attention Heads Per Token](https://aclanthology.org/2022.emnlp-main.278) (Zhang et al., EMNLP 2022)
ACL
- Xiaofeng Zhang, Yikang Shen, Zeyu Huang, Jie Zhou, Wenge Rong, and Zhang Xiong. 2022. Mixture of Attention Heads: Selecting Attention Heads Per Token. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 4150–4162, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.