Eliciting and Understanding Cross-task Skills with Task-level Mixture-of-Experts

Qinyuan Ye, Juan Zha, Xiang Ren


Abstract
Recent works suggest that transformer models are capable of multi-tasking on diverse NLP tasks and adapt to new tasks efficiently. However, the potential of these multi-task models may be limited as they use the same set of parameters for all tasks. In contrast, humans tackle tasks in a more flexible way, by making proper presumptions on what skills and knowledge are relevant and executing only the necessary computations. Inspired by this, we propose to use task-level mixture-of-expert models, which has a collection of transformer layers (i.e., experts) and a router component to choose among these experts dynamically and flexibly. We find that these models help improve the average performance gain (ARG) metric by 2.6% when adapting to unseen tasks in few-shot settings, and by 5.6% in zero-shot generalization settings. Further, we show that the learned routing decisions and experts partly rediscover human categorization of NLP tasks – certain experts are strongly associated with extractive tasks, some with classification tasks, and some with tasks requiring world knowledge.
Anthology ID:
2022.findings-emnlp.189
Volume:
Findings of the Association for Computational Linguistics: EMNLP 2022
Month:
December
Year:
2022
Address:
Abu Dhabi, United Arab Emirates
Editors:
Yoav Goldberg, Zornitsa Kozareva, Yue Zhang
Venue:
Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
2567–2592
Language:
URL:
https://aclanthology.org/2022.findings-emnlp.189
DOI:
10.18653/v1/2022.findings-emnlp.189
Bibkey:
Cite (ACL):
Qinyuan Ye, Juan Zha, and Xiang Ren. 2022. Eliciting and Understanding Cross-task Skills with Task-level Mixture-of-Experts. In Findings of the Association for Computational Linguistics: EMNLP 2022, pages 2567–2592, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.
Cite (Informal):
Eliciting and Understanding Cross-task Skills with Task-level Mixture-of-Experts (Ye et al., Findings 2022)
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PDF:
https://aclanthology.org/2022.findings-emnlp.189.pdf
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