This 2000 paper introduces a novel solution to a weakness found in Long Short-Term Memory (LSTM) networks, specifically when processing continuous data streams without predefined segmentation. The core problem addressed is the unbounded growth of internal cell states within standard LSTM networks, which can lead to performance degradation. The authors propose and implement "forget gates", an adaptive mechanism that allows LSTM cells to learn when to reset their internal memory at appropriate times, thus managing resources effectively. Through experiments with complex, continual versions of benchmark problems, the paper demonstrates that LSTMs equipped with these forget gates successfully overcome limitations faced by standard LSTMs and other recurrent neural networks. Ultimately, the work highlights the importance of adaptive forgetting for neural networks dealing with ongoing, unsegmented input.