Multi-Element Long Distance Dependencies: Using SPk Languages to Explore the Characteristics of Long-Distance Dependencies
This work addresses the challenge of understanding and modeling multi-element long-distance dependencies in datasets, which is incremental as it builds on existing methods to analyze LDD properties.
The paper tackled the problem of modeling Long Distance Dependencies (LDDs) by analyzing their characteristics using Strictly k-Piecewise languages, revealing that the number of interacting elements is a key factor, and suggested that attention mechanisms in neural networks may help but require improvement.
In order to successfully model Long Distance Dependencies (LDDs) it is necessary to understand the full-range of the characteristics of the LDDs exhibited in a target dataset. In this paper, we use Strictly k-Piecewise languages to generate datasets with various properties. We then compute the characteristics of the LDDs in these datasets using mutual information and analyze the impact of factors such as (i) k, (ii) length of LDDs, (iii) vocabulary size, (iv) forbidden subsequences, and (v) dataset size. This analysis reveal that the number of interacting elements in a dependency is an important characteristic of LDDs. This leads us to the challenge of modelling multi-element long-distance dependencies. Our results suggest that attention mechanisms in neural networks may aide in modeling datasets with multi-element long-distance dependencies. However, we conclude that there is a need to develop more efficient attention mechanisms to address this issue.