NUIG at TIAD: Combining Unsupervised NLP and Graph Metrics for Translation Inference

John Philip McCrae, Mihael Arcan

Abstract
In this paper, we present the NUIG system at the TIAD shard task. This system includes graph-based metrics calculated using novel algorithms, with an unsupervised document embedding tool called ONETA and an unsupervised multi-way neural machine translation method. The results are an improvement over our previous system and produce the highest precision among all systems in the task as well as very competitive F-Measure results. Incorporating features from other systems should be easy in the framework we describe in this paper, suggesting this could very easily be extended to an even stronger result.
Anthology ID:
2020.globalex-1.15
Volume:
Proceedings of the 2020 Globalex Workshop on Linked Lexicography
Month:
May
Year:
2020
Address:
Marseille, France
Editors:
Ilan Kernerman, Simon Krek, John P. McCrae, Jorge Gracia, Sina Ahmadi, Besim Kabashi
Venue:
GLOBALEX
SIG:
Publisher:
European Language Resources Association
Note:
Pages:
92–97
Language:
English
URL:
https://aclanthology.org/2020.globalex-1.15
DOI:
Bibkey:
Cite (ACL):
John Philip McCrae and Mihael Arcan. 2020. NUIG at TIAD: Combining Unsupervised NLP and Graph Metrics for Translation Inference. In Proceedings of the 2020 Globalex Workshop on Linked Lexicography, pages 92–97, Marseille, France. European Language Resources Association.
Cite (Informal):
NUIG at TIAD: Combining Unsupervised NLP and Graph Metrics for Translation Inference (McCrae & Arcan, GLOBALEX 2020)
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