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Much sequential data exhibits highly non-uniform information distribution.This cannot be correctly modeled by traditional Long Short-Term Memory (LSTM).To address that, recent works have extended LSTM by adding more activationsbetween adjacent inputs. However, the approaches often use a fixed depth, whichis at the step of the most information content. This one-size-fits-allworst-case approach is not satisfactory, because when little information isdistributed to some steps, shallow structures can achieve faster convergenceand consume less computation resource. In this paper, we develop aDepth-Adaptive Long Short-Term Memory (DA-LSTM) architecture, which candynamically adjust the structure depending on information distribution withoutprior knowledge. Experimental results on real-world datasets show that DA-LSTMcosts much less computation resource and substantially reduce convergence timeby $41.78\%$ and $46.01 \%$, compared with Stacked LSTM and Deep TransitionLSTM, respectively.

DA-LSTM: A Long Short-Term Memory with Depth Adaptive to Non-uniform Information Flow in Sequential Data