Parameter Sharing Methods for Multilingual Self-Attentional Translation Models

EMNLP 2018 Devendra Singh SachanGraham Neubig

In multilingual neural machine translation, it has been shown that sharing a single translation model between multiple languages can achieve competitive performance, sometimes even leading to performance gains over bilingually trained models. However, these improvements are not uniform; often multilingual parameter sharing results in a decrease in accuracy due to translation models not being able to accommodate different languages in their limited parameter space... (read more)

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