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Current generation of memory-augmented neural networks has limitedscalability as they cannot efficiently process data that are too large to fitin the external memory storage. One example of this is lifelong learningscenario where the model receives unlimited length of data stream as an inputwhich contains vast majority of uninformative entries. We tackle this problemby proposing a memory network fit for long-term lifelong learning scenario,which we refer to as Long-term Episodic Memory Networks (LEMN), that features aRNN-based retention agent that learns to replace less important memory entriesbased on the retention probability generated on each entry that is learned toidentify data instances of generic importance relative to other memory entries,as well as its historical importance. Such learning of retention agent allowsour long-term episodic memory network to retain memory entries of genericimportance for a given task. We validate our model on a path-finding task aswell as synthetic and real question answering tasks, on which our modelachieves significant improvements over the memory augmented networks withrule-based memory scheduling as well as an RL-based baseline that does notconsider relative or historical importance of the memory.

Learning What to Remember: Long-term Episodic Memory Networks for Learning from Streaming Data