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

Abhijit Mahalunkar, John Kelleher


Abstract
In order to successfully model Long Distance Dependencies (LDDs) it is necessary to under-stand 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 strings, 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.
Anthology ID:
W19-3904
Volume:
Proceedings of the Workshop on Deep Learning and Formal Languages: Building Bridges
Month:
August
Year:
2019
Address:
Florence
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
34–43
Language:
URL:
https://aclanthology.org/W19-3904
DOI:
10.18653/v1/W19-3904
Bibkey:
Cite (ACL):
Abhijit Mahalunkar and John Kelleher. 2019. Multi-Element Long Distance Dependencies: Using SPk Languages to Explore the Characteristics of Long-Distance Dependencies. In Proceedings of the Workshop on Deep Learning and Formal Languages: Building Bridges, pages 34–43, Florence. Association for Computational Linguistics.
Cite (Informal):
Multi-Element Long Distance Dependencies: Using SPk Languages to Explore the Characteristics of Long-Distance Dependencies (Mahalunkar & Kelleher, ACL 2019)
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PDF:
https://preview.aclanthology.org/ingestion-script-update/W19-3904.pdf