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In order to successfully model Long Distance Dependencies (LDDs) it isnecessary to understand the full-range of the characteristics of the LDDsexhibited in a target dataset. In this paper, we use Strictly k-Piecewiselanguages to generate datasets with various properties. We then compute thecharacteristics of the LDDs in these datasets using mutual information andanalyze the impact of factors such as (i) k, (ii) length of LDDs, (iii)vocabulary size, (iv) forbidden subsequences, and (v) dataset size. Thisanalysis reveal that the number of interacting elements in a dependency is animportant characteristic of LDDs. This leads us to the challenge of modellingmulti-element long-distance dependencies. Our results suggest that attentionmechanisms in neural networks may aide in modeling datasets with multi-elementlong-distance dependencies. However, we conclude that there is a need todevelop more efficient attention mechanisms to address this issue.

Multi-Element Long Distance Dependencies: Using SPk Languages to Explore the Characteristics of Long-Distance Dependencies