Insulating Distributional Semantic Models from Catastrophic Interference

AbstractPredictive neural networks are currently the most popular architecture for learning distributional semantics in the fields of machine learning and cognitive science. However, a major weakness of this architecture is catastrophic interference (CI): The sudden and complete loss of previously learned associations when encoding new ones. CI is an issue with backpropagation; when learning sequential data, the error signal dramatically modifies the connection weights between nodes—causing rapid forgetting. CI is a huge problem for predictive semantic models of word meaning, because multiple word senses interfere with each other. Here, we evaluate a recently proposed solution to CI from neuroscience, elastic weight consolidation, as well as a Hebbian learning architecture from the memory literature that does not produce an error signal. Both solutions are evaluated on an artificial and natural language task in their ability to insulate a previously learned sense of a word when learning a new one.


Return to previous page