Papers by Graeme Blackwood

2 papers
Synthetic Pre-Training Tasks for Neural Machine Translation (2023.findings-acl)

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Challenge: toxicity and bias can be addressed by pre-training with synthetic resources . BLEU scores are used to compare methods with real-world data .
Approach: They propose several ways to generate obfuscated data from large parallel corpus and concatenating phrase pairs from small word-aligned corpus with synthetic parallel data without real human language corpora.
Outcome: The proposed methods can be used to generate obfuscated data or synthetic parallel data without real human language corpora even with high levels of oblication.
Multilingual Neural Machine Translation with Task-Specific Attention (C18-1)

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Challenge: Multilingual machine translation is a task of building a system capable of translating between multiple source and target languages.
Approach: They propose task-specific attention models to retain parameter sharing generalization . they observe improved translation quality even in low-resource zero-shot directions .
Outcome: The proposed model retains parameter sharing generalization while allowing language-specific specialization . it improves translation quality even in low-resource zero-shot translation directions .

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