Papers with text-based machine translation

2 papers
Generating Synthetic Speech from SpokenVocab for Speech Translation (2023.findings-eacl)

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Challenge: End-to-end speech-totext translation (ST) models require large amounts of data to train, but their size is considerably smaller than text-based MT data.
Approach: They propose a method to convert MT data to ST data via text-to-speech systems.
Outcome: The proposed method improves translation quality by an average of 1.83 BLEU score while performing equally well as TTS-generated speech in improving translation quality.
Beyond Sentence-Level End-to-End Speech Translation: Context Helps (2021.acl-long)

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Challenge: Document-level contextual information has shown benefits to text-based machine translation, but whether and how it helps end-to-end speech translation is still under-studied.
Approach: They propose a concatenation-based ST model with adaptive feature selection for computational efficiency.
Outcome: The proposed model improves translation quality and robustness to (artificial) audio segmentation errors.

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