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In recent years, with the advent of massive computational power and theavailability of huge amounts of data, Deep neural networks have enabled theexploration of uncharted areas in several domains. But at times, theyunder-perform due to insufficient data, poor data quality, data that might notbe covering the domain broadly, etc. Knowledge-based systems leverage expertknowledge for making decisions and suitably take actions. Such systems retaininterpretability in the decision-making process. This paper focuses onexploring techniques to integrate expert knowledge to the Deep Neural Networksfor sequence-to-sequence and time series models to improve their performanceand interpretability.

A Survey on Knowledge integration techniques with Artificial Neural Networks for seq-2-seq/time series models