Nested LSTMs
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We propose Nested LSTMs (NLSTM), a novel RNN architecture with multiple levels of memory. Nested LSTMs add depth to LSTMs via nesting as opposed to stacking. The value of a memory cell in an NLSTM is computed by an LSTM cell, which has its own inner memory cell. Specifically, instead of computing the value of the (outer) memory cell as c^outer_t = f_t c_t-1 + i_t g_t, NLSTM memory cells use the concatenation (f_t c_t-1, i_t g_t) as input to an inner LSTM (or NLSTM) memory cell, and set c^outer_t = h^inner_t. Nested LSTMs outperform both stacked and single-layer LSTMs with similar numbers of parameters in our experiments on various character-level language modeling tasks, and the inner memories of an LSTM learn longer term dependencies compared with the higher-level units of a stacked LSTM.
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