Learning Simpler Language Models with the Differential State Framework
This addresses the need for simpler, high-performing models in language modeling, though it appears incremental as it builds on existing gated architectures.
The paper tackles the problem of learning across long time lags in temporal neural models by introducing the Differential State Framework (DSF) and Delta-RNN, which outperforms LSTM and GRU in word and character-level language modeling and matches state-of-the-art baselines when regularized.
Learning useful information across long time lags is a critical and difficult problem for temporal neural models in tasks such as language modeling. Existing architectures that address the issue are often complex and costly to train. The Differential State Framework (DSF) is a simple and high-performing design that unifies previously introduced gated neural models. DSF models maintain longer-term memory by learning to interpolate between a fast-changing data-driven representation and a slowly changing, implicitly stable state. This requires hardly any more parameters than a classical, simple recurrent network. Within the DSF framework, a new architecture is presented, the Delta-RNN. In language modeling at the word and character levels, the Delta-RNN outperforms popular complex architectures, such as the Long Short Term Memory (LSTM) and the Gated Recurrent Unit (GRU), and, when regularized, performs comparably to several state-of-the-art baselines. At the subword level, the Delta-RNN's performance is comparable to that of complex gated architectures.