December 06, 2021
Multi-head attention has each of the attention heads collect salient information from different parts of an input sequence, making it a powerful mechanism for sequence modeling. Multilingual and multi-domain learning are common scenarios for sequence modeling, where the key challenge is to maximize positive transfer and mitigate negative interference across languages and domains. In this paper, we find that non-selective attention sharing is sub-optimal for achieving good generalization across all languages and domains. We further propose attention sharing strategies to facilitate parameter sharing and specialization in multilingual and multi-domain sequence modeling. Our approach automatically learns shared and specialized attention heads for different languages and domains. Evaluated in various tasks including speech recognition, text-to-text and speech-to-text translation, the proposed attention sharing strategies consistently bring gains to sequence models built upon multi-head attention. For speech-to-text translation, our approach yields an average of $+2.0$ BLEU over $13$ language directions in multilingual setting and $+2.0$ BLEU over $3$ domains in multi-domain setting.
Publisher
NeurIPS
Research Topics
April 22, 2024
Vasu Sharma *, Karthik Padthe *, Newsha Ardalani, Kushal Tirumala, Russ Howes, Hu Xu, Bernie Huang, Daniel Li (FAIR), Armen Aghajanyan, Gargi Ghosh, Luke Zettlemoyer
April 22, 2024
April 14, 2024
Heng-Jui Chang, Ning Dong (AI), Ruslan Mavlyutov, Sravya Popuri, Andy Chung
April 14, 2024
April 05, 2024
Suyu Ge, Chunting Zhou, Rui Hou, Madian Khabsa, Yi-Chia Wang, Qifan Wang, Jiawei Han, Yuning Mao
April 05, 2024
February 21, 2024
Tom Sander, Pierre Fernandez, Alain Durmus, Matthijs Douze, Teddy Furon
February 21, 2024
Product experiences
Foundational models
Product experiences
Latest news
Foundational models