Papers by Tatiana Likhomanenko

1 papers
Joint Speech Transcription and Translation: Pseudo-Labeling with Out-of-Distribution Data (2023.findings-acl)

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Challenge: a recent study shows that self-training can improve upon fully supervised baselines in low-resource settings for several sequence-to-sequence tasks.
Approach: They propose to use pseudo-labeling to label unsupervised data and add it to the training pool.
Outcome: The proposed setup improves on the unsupervised data by using pseudo-labeling . the proposed setup provides 0.4% absolute WER and 2.1 BLEU points for En–De .

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