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Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume
Sequential fine-tuning and multi-task learning are methods aiming to incorporate knowledge from multiple tasks; however, they suffer from catastrophic forgetting and difficulties in dataset balancing. To address these shortcomings, we propose AdapterFusion, a new two stage learning algorithm that leverages knowledge from multiple tasks. First, in the knowledge extraction stage we learn task specific parameters called adapters, that encapsulate the task-specific information. We then combine thedoi:10.18653/v1/2021.eacl-main.39 fatcat:ceclmygggnehlps66t6iicwmtq