Papers by Joris Vanvinckenroye

2 papers
Detecting Various Types of Noise for Neural Machine Translation (2022.findings-acl)

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Challenge: a recent study investigated the impact of noise on the performance of machine translation systems.
Approach: They propose to combine recent research on data filtering with original analysis . they find that most of the suggested noise types can be detected with 90% accuracy .
Outcome: The proposed filtering systems can detect noise types with 90% accuracy in high resource settings.
Data Filtering using Cross-Lingual Word Embeddings (2021.naacl-main)

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Challenge: varying task definitions and data conditions make it difficult to draw a meaningful comparison.
Approach: They propose to use language identification to perform data filtering on MT data based on cross-lingual word embeddings to identify weaknesses in language identification tool.
Outcome: The proposed methods perform well on three real-life, high resource MT tasks while performing weakly within more realistic task conditions.

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