Low-Rank Adaptation Boosts Continual Learning in Neural Machine Translation

This research explores efficient continual learning for neural machine translation, utilizing low-rank adaptation. The work likely addresses the catastrophic forgetting problem, crucial for NMT models adapting to new data streams.

• •Applies low-rank adaptation to neural machine translation.
• •Addresses the challenge of continual learning in NMT.
• •Potentially improves model performance and adaptability.