Continual Joint Event Extraction via Adaptive Bias Mixing

Chengeng Lu, Yatian Shen

Abstract: Continual event extraction is a practical task in natural language processing that requires models to learn quickly from new event types and data sources without forgetting pre-existing knowledge. It is important since language models often encounter new information to which they must efficiently adapt. Current work on continual event extraction either reuses existing parameters to learn from new tasks or statically assigns parameters specific to each new incoming task, incurring significant computational cost while preventing the potential knowledge sharing between tasks. To achieve a balance between the two, in this work, we present a means to adapt the model to incoming tasks in a parameter-efficient manner. We also incorporate metric learning to construct a prototypical network for maximum parameter efficiency. Experimental results on the ACE2005 dataset show that our framework maintains baseline performance with significantly smaller parameter sizes.

External IDs:dblp:conf/bdcloud/LuS24