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NUIG at TIAD: Combining unsupervised NLP and graph metrics for translation inference

McCrae, John P.
; 
Arcan, Mihael
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Identifiers
http://hdl.handle.net/10379/16057
 https://doi.org/10.13025/21327
Publication Date
2020-05-11
Keywords
translation inference, machine translation, multiway translation, document embeddings
Type
Workshop paper
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Citation
McCrae, John P., & Arcan, Mihael. (2020). NUIG at TIAD: Combining unsupervised NLP and graph metrics for translation inference. Paper presented at the Language Resources and Evaluation Conference (LREC 2020) Globalex Workshop on Linked Lexicography, Marseille, 11-16 May.
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.
Funder
Science Foundation Ireland
European Regional Development Fund
Horizon 2020
Publisher
European Language Resources Association (ELRA)
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Rights
Attribution-NonCommercial-NoDerivs 3.0 Ireland
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Collections
Data Science Institute (Workshop Papers)