Surprisal-Driven Feedback in Recurrent Networks
This work addresses the need for better context-aware prediction in recurrent networks, offering a domain-specific improvement for sequence modeling tasks.
The paper tackled the problem of improving recurrent neural networks for temporal data prediction by incorporating top-down feedback based on past error information, resulting in a performance of 1.37 BPC on the enwik8 character-level prediction task.
Recurrent neural nets are widely used for predicting temporal data. Their inherent deep feedforward structure allows learning complex sequential patterns. It is believed that top-down feedback might be an important missing ingredient which in theory could help disambiguate similar patterns depending on broader context. In this paper we introduce surprisal-driven recurrent networks, which take into account past error information when making new predictions. This is achieved by continuously monitoring the discrepancy between most recent predictions and the actual observations. Furthermore, we show that it outperforms other stochastic and fully deterministic approaches on enwik8 character level prediction task achieving 1.37 BPC on the test portion of the text.