@inproceedings{liang-etal-2025-thor,
title = "{THOR}-{M}o{E}: Hierarchical Task-Guided and Context-Responsive Routing for Neural Machine Translation",
author = "Liang, Yunlong and
Meng, Fandong and
Zhou, Jie",
editor = "Che, Wanxiang and
Nabende, Joyce and
Shutova, Ekaterina and
Pilehvar, Mohammad Taher",
booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.acl-long.1040/",
doi = "10.18653/v1/2025.acl-long.1040",
pages = "21433--21445",
ISBN = "979-8-89176-251-0",
abstract = "The sparse Mixture-of-Experts (MoE) has achieved significant progress for neural machine translation (NMT). However, there exist two limitations in current MoE solutions which may lead to sub-optimal performance: 1) they directly use the task knowledge of NMT into MoE (\textit{e.g.}, domain/linguistics-specific knowledge), which are generally unavailable at practical application and neglect the naturally grouped domain/linguistic properties; 2) the expert selection only depends on the localized token representation without considering the context, which fully grasps the state of each token in a global view. To address the above limitations, we propose THOR-MoE via arming the MoE with hierarchical task-guided and context-responsive routing policies. Specifically, it 1) firstly predicts the domain/language label and then extracts mixed domain/language representation to allocate task-level experts in a hierarchical manner; 2) injects the context information to enhance the token routing from the pre-selected task-level experts set, which can help each token to be accurately routed to more specialized and suitable experts. Extensive experiments on multi-domain translation and multilingual translation benchmarks with different architectures consistently demonstrate the superior performance of THOR-MoE. Additionally, the THOR-MoE operates as a plug-and-play module compatible with existing Top-(CITATION) or Top-(CITATION) routing schemes, ensuring broad applicability across diverse MoE architectures. For instance, compared with vanilla Top- (CITATION) routing, the context-aware manner can achieve an average improvement of 0.75 BLEU with less than 22{\%} activated parameters on multi-domain translation tasks."
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<abstract>The sparse Mixture-of-Experts (MoE) has achieved significant progress for neural machine translation (NMT). However, there exist two limitations in current MoE solutions which may lead to sub-optimal performance: 1) they directly use the task knowledge of NMT into MoE (e.g., domain/linguistics-specific knowledge), which are generally unavailable at practical application and neglect the naturally grouped domain/linguistic properties; 2) the expert selection only depends on the localized token representation without considering the context, which fully grasps the state of each token in a global view. To address the above limitations, we propose THOR-MoE via arming the MoE with hierarchical task-guided and context-responsive routing policies. Specifically, it 1) firstly predicts the domain/language label and then extracts mixed domain/language representation to allocate task-level experts in a hierarchical manner; 2) injects the context information to enhance the token routing from the pre-selected task-level experts set, which can help each token to be accurately routed to more specialized and suitable experts. Extensive experiments on multi-domain translation and multilingual translation benchmarks with different architectures consistently demonstrate the superior performance of THOR-MoE. Additionally, the THOR-MoE operates as a plug-and-play module compatible with existing Top-(CITATION) or Top-(CITATION) routing schemes, ensuring broad applicability across diverse MoE architectures. For instance, compared with vanilla Top- (CITATION) routing, the context-aware manner can achieve an average improvement of 0.75 BLEU with less than 22% activated parameters on multi-domain translation tasks.</abstract>
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%0 Conference Proceedings
%T THOR-MoE: Hierarchical Task-Guided and Context-Responsive Routing for Neural Machine Translation
%A Liang, Yunlong
%A Meng, Fandong
%A Zhou, Jie
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-251-0
%F liang-etal-2025-thor
%X The sparse Mixture-of-Experts (MoE) has achieved significant progress for neural machine translation (NMT). However, there exist two limitations in current MoE solutions which may lead to sub-optimal performance: 1) they directly use the task knowledge of NMT into MoE (e.g., domain/linguistics-specific knowledge), which are generally unavailable at practical application and neglect the naturally grouped domain/linguistic properties; 2) the expert selection only depends on the localized token representation without considering the context, which fully grasps the state of each token in a global view. To address the above limitations, we propose THOR-MoE via arming the MoE with hierarchical task-guided and context-responsive routing policies. Specifically, it 1) firstly predicts the domain/language label and then extracts mixed domain/language representation to allocate task-level experts in a hierarchical manner; 2) injects the context information to enhance the token routing from the pre-selected task-level experts set, which can help each token to be accurately routed to more specialized and suitable experts. Extensive experiments on multi-domain translation and multilingual translation benchmarks with different architectures consistently demonstrate the superior performance of THOR-MoE. Additionally, the THOR-MoE operates as a plug-and-play module compatible with existing Top-(CITATION) or Top-(CITATION) routing schemes, ensuring broad applicability across diverse MoE architectures. For instance, compared with vanilla Top- (CITATION) routing, the context-aware manner can achieve an average improvement of 0.75 BLEU with less than 22% activated parameters on multi-domain translation tasks.
%R 10.18653/v1/2025.acl-long.1040
%U https://aclanthology.org/2025.acl-long.1040/
%U https://doi.org/10.18653/v1/2025.acl-long.1040
%P 21433-21445
Markdown (Informal)
[THOR-MoE: Hierarchical Task-Guided and Context-Responsive Routing for Neural Machine Translation](https://aclanthology.org/2025.acl-long.1040/) (Liang et al., ACL 2025)
ACL