NUIG at TIAD: Combining unsupervised NLP and graph metrics for translation inference
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2020-05-11Author
McCrae, John P.
Arcan, Mihael
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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.
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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.