The Importance of the Current Input in Sequence Modeling
This addresses a bottleneck in sequence modeling for NLP applications, offering a simple yet effective enhancement to existing methods.
The paper tackles the problem of improving prediction accuracy in sequence modeling for natural language processing by adding a direct connection between input and output, skipping the recurrent module, which leads to a new state-of-the-art perplexity in language modeling.
The last advances in sequence modeling are mainly based on deep learning approaches. The current state of the art involves the use of variations of the standard LSTM architecture, combined with several tricks that improve the final prediction rates of the trained neural networks. However, in some cases, these adaptations might be too much tuned to the particular problems being addressed. In this article, we show that a very simple idea, to add a direct connection between the input and the output, skipping the recurrent module, leads to an increase of the prediction accuracy in sequence modeling problems related to natural language processing. Experiments carried out on different problems show that the addition of this kind of connection to a recurrent network always improves the results, regardless of the architecture and training-specific details. When this idea is introduced into the models that lead the field, the resulting networks achieve a new state-of-the-art perplexity in language modeling problems.